Tactile-Guided Diffusion Editing

• Tactile-guided diffusion editing is a method leveraging physically grounded tactile signals to steer diffusion processes for generating visually and geometrically consistent outputs.
• It integrates contact-conditioned tactile image synthesis with touch-conditioned latent diffusion to model deformation, marker motion, and local contact evidence.
• The approach employs early fusion of force data and iterative latent refinement, reconciling global generative priors with localized tactile information.

Vision-based tactile sensing and touch-conditioned generative modeling have begun to define a technical basis for tactile-guided diffusion editing: the use of tactile or contact-derived signals to steer diffusion-based generation or modification so that outputs satisfy physically grounded contact constraints. In the current literature, the clearest formulations arise in two adjacent settings. One is contact-conditioned generation of vision-based tactile sensor images from object appearance and force state, where diffusion is conditioned on RGB object imagery and six-axis force/torque measurements to synthesize tactile observations (Lin et al., 2024). The other is touch-conditioned latent diffusion for 3D reconstruction, where sequential tactile observations iteratively refine a latent object hypothesis and improve local geometric fidelity (Wang et al., 19 May 2025). Although neither work is framed as generic “editing” in the image-manipulation sense, together they establish the principal ingredients of tactile-guided diffusion editing: physically meaningful tactile conditioning, iterative latent refinement under touch evidence, and explicit mechanisms for reconciling global generative priors with local contact-induced structure.

1. Conceptual scope and definition

“Tactile-guided diffusion editing” may be understood as diffusion-based modification of visual, tactile, or geometric representations under guidance from tactile/contact signals rather than purely semantic prompts. In the most direct existing formulation, the conditioning variables are the contacting object image and a six-axis force/torque vector,

$(F_x, F_y, F_z, M_x, M_y, M_z),$

which jointly determine the tactile image to be synthesized by a contact condition-guided diffusion model (Lin et al., 2024). In a 3D formulation, the conditioning signal is a sequence of tactile observations $T_0,\dots,T_{n-1}$, transformed into geometric touch tokens that guide latent denoising and update a compact 3D shape hypothesis over time (Wang et al., 19 May 2025).

The defining property of this family of methods is that conditioning is physically grounded. The tactile signal is not an auxiliary tag or abstract style code; it represents contact state, local geometry, or force-dependent deformation. This makes tactile-guided diffusion editing distinct from text-guided editing, classifier-guided sampling, or weakly conditioned image translation. In the tactile setting, the goal is not merely to alter appearance, but to enforce consistency with contact mechanics, deformation patterns, marker motion, or local shape evidence.

The current literature suggests two operational meanings of “editing.” The first is full synthesis under tactile constraints, as in tactile image generation from object RGB imagery and force data (Lin et al., 2024). The second is iterative correction of a latent geometric representation as new touches arrive, which functions as a form of sequential latent editing for 3D reconstruction (Wang et al., 19 May 2025). A plausible implication is that future tactile-guided diffusion editors will unify these two modes: global diffusion-based priors for plausible structure, coupled with localized tactile constraints for physically consistent edits.

2. Contact-conditioned tactile image synthesis

A concrete foundation for tactile-guided diffusion editing is the contact-condition guided diffusion model for vision-based tactile image generation (Lin et al., 2024). The task is to map a representation of the contacting object and the applied contact load to the corresponding tactile sensor image that would be observed by a real vision-based tactile sensor. The inputs are an RGB image of the contacting object, denoted $I$, and a six-axis force/torque vector $F$. The output is an RGB tactile image, whose exact appearance depends on sensor type: three-channel RGB-light tactile images without markers, RGB-light tactile images with markers, or white-light tactile images with markers.

The central claim is that optical tactile sensor images are determined jointly by object-side geometry, texture, posture, and position, together with the contact force state, which affects deformation extent, brightness and shadow distribution, and marker motion. The learned mapping is therefore

$$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$

This formulation is explicitly conditional diffusion: the model starts from noise and denoises toward a tactile image while being guided by contact conditions (Lin et al., 2024).

This setup is directly relevant to editing because it demonstrates that force-conditioned image synthesis can be achieved without explicit optical or mechanical simulation. The paper argues that the diffusion model learns the sensor response directly from paired real data rather than relying on hand-crafted simulators. This suggests a general editing principle: contact-aware modification can be learned as a data-driven conditional prior over sensor responses or contact-induced appearance, instead of being derived from explicit rendering or mechanics models.

The same work also makes clear that tactile effects are not reducible to simple intensity scaling. Increasing normal force increases deformation area, changing tangential force alters shadow distribution within the contact region, and object geometry and force are coupled; for example, spheres of different diameters can produce similar projected areas under some conditions but differ in deformation patterns (Lin et al., 2024). That observation is central to the editing interpretation: tactile guidance changes structured local image content in a geometry-dependent way.

3. Conditioning mechanisms in diffusion models

The conditioning mechanism in the tactile image generator is deliberately simple. The object image $I$ has size $(3,256,256)$, while the force vector $F$ is expanded into a spatial tensor through a hash function $H(\cdot)$, producing a tensor of size $(1,256,256)$. The conditioning tensor is

$T_0,\dots,T_{n-1}$0

The resulting tensor $T_0,\dots,T_{n-1}$1 has size $T_0,\dots,T_{n-1}$2 and is passed into a U-Net denoising backbone by early fusion through channel concatenation (Lin et al., 2024).

The denoising function is written as

$T_0,\dots,T_{n-1}$3

where $T_0,\dots,T_{n-1}$4 is the contact condition tensor, $T_0,\dots,T_{n-1}$5 is the noisy tactile image at diffusion step $T_0,\dots,T_{n-1}$6, and $T_0,\dots,T_{n-1}$7 is the “statistical noise quantity.” The model iteratively refines the noisy image toward the tactile image $T_0,\dots,T_{n-1}$8 corresponding to the contact conditions (Lin et al., 2024). The training loss is

$T_0,\dots,T_{n-1}$9

Several negative architectural facts are also informative. The paper does not mention cross-attention, conditional normalization, ControlNet-style side branches, classifier-free guidance, latent diffusion, variational autoencoding, textual conditioning, or explicit inpainting masks (Lin et al., 2024). Conditioning is therefore best described as early fusion by channel concatenation.

A second conditioning paradigm appears in touch-conditioned latent diffusion for 3D reconstruction (Wang et al., 19 May 2025). There, diffusion operates in the latent space of a 3D VQ-VAE rather than directly on pixels or voxels. Tactile images are first mapped, via a pretrained TouchCNN, to touch charts encoding local geometric patches. These charts are merged into a tensor of shape $I$0, where each of the $I$1 vertices stores $I$2 coordinates and touch status. Each chart becomes a token after convolution, pooling, and centroid-based positional encoding. In the visual-tactile variant, image features extracted using ResNet are turned into feature tokens and combined with touch tokens via a dropout layer before entering the denoising network (Wang et al., 19 May 2025).

The latent diffusion loss is

$I$3

where $I$4 is the noised latent, $I$5 is the time embedding, and $I$6 is the touch condition extraction network output (Wang et al., 19 May 2025). This shows that tactile information enters the denoiser directly rather than being used only to initialize a latent that is later denoised unconditionally.

Taken together, these works show two reusable conditioning patterns for tactile-guided diffusion editing: spatialized force channels concatenated with image input, and touch-token conditioning of a latent denoiser. The former is simple and physically interpretable; the latter is better suited to sequential local geometric refinement.

4. Data representations and supervision regimes

The contact-conditioned tactile image generator is trained on paired real data (Lin et al., 2024). The acquisition system contains a force gauge, a displacement platform with $I$7, $I$8, $I$9 stages, a rotary stage, and the tactile sensor. The object is mounted to the force gauge probe and brought into contact with the sensor. The force gauge records six-axis contact state, and the platform executes controlled movements: initial contact at $F$0, downward displacement by $F$1 mm, motion along $F$2 and $F$3, repetition in opposite directions, twisting by $F$4 around the $F$5-axis, and repetition until $F$6 mm. Data are captured at $F$7 Hz using external triggers, with motion performed slowly at $F$8 mm/s to reduce rate-dependent nonlinear effects (Lin et al., 2024).

For each object, the dataset contains paired tactile image, object image, and six-axis force vector. The image size is $F$9, each object has approximately 700 pairs of contact information data, and the dataset is split in an 8:2 train/test ratio (Lin et al., 2024). The supervision is therefore strongly aligned and physically grounded, but data-intensive.

By contrast, Touch2Shape is trained entirely in simulation (Wang et al., 19 May 2025). It uses two datasets: an ABC-based dataset with 40,000 objects, and a ShapeNet-based tactile dataset from TouchSDF with 1650 objects across six categories—bowl, bottle, camera, jar, guitar, and mug—split into 1100 train, 200 val, and 350 test. All tactile images and initial RGB images are rendered in simulation using the environment from ActiveVT (Wang et al., 19 May 2025). The 3D representation is a volumetric T-SDF at resolution $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$0, encoded and decoded by a 3D VQ-VAE: $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$1

These supervision regimes define two complementary data models for tactile-guided diffusion editing. One uses real paired observations to learn sensor-specific tactile appearance faithfully (Lin et al., 2024). The other uses simulated touch to learn latent geometric refinement at scale (Wang et al., 19 May 2025). This suggests a methodological divide in the field: appearance-faithful tactile editing may depend on expensive real paired data, whereas geometric tactile editing can presently be explored more extensively in simulation.

5. Evaluation criteria and empirical performance

The tactile image generation work evaluates generated tactile images against real tactile images using MSE, MAE, SSIM, and PSNR (Lin et al., 2024). The main quantitative comparison is as follows.

Method	MSE	SSIM
TACTO 28	201.87	0.80
Taxim 18	56.715	0.88
FOTS 19	53.267	0.89
Ours (RGB-light)	21.00	0.97
Ours (RGB-light Mark)	28.02	0.95
Ours (White-light Mark)	67.09	0.81

The best reported performance is on RGB-light tactile images without markers, with MSE $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$2, SSIM $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$3, and PSNR $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$4 (Lin et al., 2024). The paper states that compared with FOTS for RGB-light tactile images without markers, the method reduces MSE by approximately 60.58%, and the abstract also reports a 38.1% reduction in marker displacement error compared to existing approaches based on lighting and mechanical models (Lin et al., 2024).

For marker-based tactile images, evaluation includes average Euclidean displacement error between marker centroids in real and generated images: $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$5 with $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$6 the number of markers. Marker detection uses adaptive thresholding and contour detection, and an $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$7 central marker grid totaling 324 markers (Lin et al., 2024). Reported RGB-light performance is an average marker position error of 91 pixels total, approximately 0.28 pixels per marker over 324 markers. This is especially relevant to editing because it measures localized deformation plausibility, not just global image similarity.

Qualitative results further show that deformation area grows as normal force increases, tangential force changes alter shadow patterns, marker optical flow trends in generated images match those in real images, and fine texture features can be reconstructed in Montessori tactile board texture generation. Reported example MSEs for different textures are 37.08, 32.56, 42.60, and 20.02 (Lin et al., 2024).

In Touch2Shape, performance is evaluated at the 3D level. On ABC, using Chamfer Distance, the reported values include 6.794 for tactile-only with grasp #1, compared to 9.016 for VTRecon and 8.220 for ActiveVT; and 1.475 for visual+tactile with grasp #0, compared to 2.653 and 2.538 respectively (Wang et al., 19 May 2025). On ShapeNet, using EMD, the average values are 0.124 for tactile-only with 1 touch and 0.056 with 10 touches, while the visual+tactile variant achieves 0.048 with 1 touch, 0.046 with 10 touches, and 0.042 with 20 touches (Wang et al., 19 May 2025). The policy evaluation reports the ratio between Chamfer Distance after 5 actions and initial Chamfer Distance; lower is better. For tactile-only, ActiveVT RL yields 23.84 while the proposed policy yields 6.63 (Wang et al., 19 May 2025).

The evaluation practices reveal an important distinction. Image-level tactile generation emphasizes fidelity to real sensor outputs, while latent tactile reconstruction emphasizes progressive correction of object geometry. For editing, these correspond to two complementary desiderata: local physical plausibility in tactile appearance and iterative convergence toward touch-consistent geometric structure.

6. Sequential refinement, fusion, and the editing interpretation

Touch2Shape provides the clearest example of tactile signals functioning as iterative corrective edits to a generative latent state (Wang et al., 19 May 2025). The system has a pretrained 3D VQ-VAE for TSDF latent modeling, a pretrained TouchCNN to predict touch charts, a contrastive touch encoder, a touch-conditioned diffusion model in latent space, a touch-shape fusion module, and an RL policy network for next-touch planning. During inference, tactile images $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$8 are converted into a condition signal $$(\text{object RGB image}, \text{six-axis force data}) \rightarrow \text{tactile image}.$$9, which conditions the latent denoiser. The denoised latent $I$0 represents the current object hypothesis, which is decoded to a TSDF and then refined using aggregated touch geometry (Wang et al., 19 May 2025).

This structure yields two levels of tactile influence. First, touch steers the global latent hypothesis through conditional denoising. Second, touch refines local geometry during decoding through the touch-shape fusion module. The fusion mechanism is introduced because diffusion outputs may remain globally plausible while still exhibiting local discrepancies relative to touch charts, and because the compressed latent may omit high-dimensional fine detail (Wang et al., 19 May 2025). Historical touch information is merged into a global touched-shape representation, voxelized, encoded, and fused with multi-scale decoder features. One fusion equation is given as

$I$1

Here $I$2 is a decoder feature map, $I$3 is a touch-voxel encoder feature map, $I$4 is a learnable weight, and $I$5 is the fused feature map (Wang et al., 19 May 2025).

The contrastive touch encoder further improves conditioning by aligning touch features and shape latent vectors in a joint embedding space using a MoCo-style objective,

$I$6

This does not directly implement editing, but it improves the ability of tactile evidence to move the latent hypothesis toward shape-consistent regions of latent space (Wang et al., 19 May 2025).

The exploration component extends this interpretation. The policy acts on the denoised latent rather than a repeatedly decoded full TSDF, using improvement in diffusion denoising loss as a binary reward: $I$7 A DQN-style loss is then used for action selection (Wang et al., 19 May 2025). This setup can be interpreted as active tactile-guided editing of the latent object representation, where new touches are chosen to maximize useful latent correction.

A plausible implication is that tactile-guided diffusion editing will benefit from coarse-to-fine architectures in which tactile conditioning modifies a compact latent globally, followed by localized tactile fusion or constrained decoding for high-frequency contact-region corrections.

7. Limitations, misconceptions, and open directions

The present literature does not yet define a standardized tactile-guided diffusion editing task. One common misconception would be to treat existing methods as complete editing systems. The tactile image generator in (Lin et al., 2024) generates tactile images from scratch under contact conditions; it does not address preservation of unedited regions, invertibility from an existing image, masked inpainting objectives, or user-controlled region editing. Likewise, Touch2Shape performs object-level reconstruction and active exploration rather than semantic editing (Wang et al., 19 May 2025).

A second misconception would be to assume that tactile conditioning is already architecturally elaborate. In the tactile image generator, the conditioning pathway is notably simple: spatialized force channels concatenated with RGB input, with no cross-attention, conditional normalization, or ControlNet-like branch (Lin et al., 2024). Its effectiveness suggests that tactile guidance need not be architecturally complex to be useful, but it does not establish that simple early fusion is sufficient for all editing settings.

Both works also have significant limitations. The tactile image generator depends on paired triplets of object image, force vector, and real tactile sensor image, which is a substantial data bottleneck (Lin et al., 2024). It does not report formal ablations isolating force conditioning, object-image conditioning, or alternative conditioning injections, and it omits full diffusion equations, beta schedules, timestep embedding details, and detailed architectural depth. Touch2Shape is simulation-only, limited to object-level reconstruction, and omits exact denoiser architecture, latent dimensionality, conditioning-path details, and explicit uncertainty modeling (Wang et al., 19 May 2025). It also evaluates on a restricted set of ShapeNet categories in the tactile benchmark.

The literature nonetheless suggests several clear research directions. One is masked or partial denoising for preserving non-contact regions during tactile-guided image modification; this is not implemented in (Lin et al., 2024), but its localized tactile effects make such an extension natural. Another is explicit contact-region or deformation-map conditioning, since the current force representation is a hash-expanded full-frame channel rather than an explicit localization signal (Lin et al., 2024). A third is hybrid global-local editing, combining latent tactile denoising with local tactile fusion, as exemplified in 3D by Touch2Shape (Wang et al., 19 May 2025). A fourth is transfer from simulation to real tactile hardware, which Touch2Shape explicitly identifies as future work (Wang et al., 19 May 2025).

In its current state, tactile-guided diffusion editing is best regarded as an emerging synthesis of two demonstrated capabilities: contact-conditioned diffusion for generating tactile-consistent imagery, and touch-conditioned latent diffusion for progressive geometric refinement. The field’s central technical insight is that tactile signals can serve as primary diffusion conditions that encode physically meaningful constraints on deformation, marker motion, and local geometry, thereby enabling generative models to be steered by contact rather than by semantics alone (Lin et al., 2024, Wang et al., 19 May 2025).