Video Diffusion–Guided Mesh Animation

• The paper introduces a novel framework that leverages video diffusion models to drive mesh animation with precise control over deformation and appearance.
• It employs transformer-based variational autoencoders and classifier-free guidance to fuse 2D video features with 3D mesh structures, ensuring high-fidelity motion.
• Quantitative benchmarks and user studies confirm significant improvements in temporal consistency and geometric fidelity, overcoming limitations of traditional techniques.

Video diffusion–guided mesh animation denotes a class of computational techniques that drive the deformation and animation of static 3D meshes using priors, correspondence maps, or motion signals extracted directly from video diffusion models or video sequences. Modern pipelines leverage deep generative video models, cross-attention to structured latent spaces, and transformer-based denoisers to generate temporally coherent, high-fidelity mesh animations compatible with graphics and rendering engines. Recent advances incorporate explicit mesh conditioning, UV-space correspondence blending, semantic feature alignment, and classifier-free guidance to achieve precise geometric control and temporal consistency. These frameworks address key limitations of both implicit and skeletal animation methods—especially rendering inefficiency, manual rigging burdens, and poor cross-category generalization—by integrating video-driven deformation into a learnable, tractable generative process.

1. Foundations and Key Challenges

Video diffusion–guided mesh animation builds on deep generative models that learn motion and appearance from large-scale video data, but adapts these models for explicit control of 3D mesh assets. Traditional 4D generation techniques typically fall into two categories: implicit neural scene methods (low rendering efficiency, not rasterization-friendly), and skeleton-based approaches (require manual rigging, poor generalization). The central challenge addressed by these methods is to animate existing 3D assets, not create new geometry or topology, demanding precise, temporally coherent deformation trajectories that preserve mesh connectivity and visual fidelity (Shi et al., 9 Jun 2025).

Pipelines must manage high-dimensional temporal trajectories, reconcile 2D observations with 3D structures, propagate correspondence and appearance across frames, and ensure compatibility with standard graphics rendering engines and downstream applications.

2. Core Architectures and Latent Representations

Principal frameworks, such as DriveAnyMesh, operate on two key inputs: a static 3D mesh (typically as an initial point cloud $P_1=\{p_i\in\mathbb R^3\}_{i=1}^N$), and a video sequence $\mathcal V=\{I_t\}_{t=1}^T$ from a fixed camera. Motion is represented in a compressed latent space: a sequence of “latent sets” $Z=\{z_0,\ldots,z_T\}$ where each $z_t\in\mathbb R^{M\times C_0}$ encodes deformation and appearance (Shi et al., 9 Jun 2025). Transformer-based variational autoencoders (VAEs) are typically employed to jointly capture shape and motion; positional embeddings are used to lift 3D points; appearance tokens from video frames are fused via cross-attention.

The denoising direction uses spatiotemporal diffusion models, which alternate spatial self-attention, cross-attention to mesh geometry, cross-attention to video features, and temporal self-attention. Conditioning signals may include video patch embeddings, motion priors extracted from generative video models, or keyframe-based correspondence injections (e.g., UV-space blending in Generative Rendering (Cai et al., 2023)).

3. Conditioning Mechanisms and Video-Driven Guidance

Video guidance is implemented by cross-attention to video-derived features at every reverse step in the diffusion process. In the generative rendering paradigm, mesh-to-UV and UV-to-image mappings propagate correspondence and semantic features across frames:

• Fixed UV-space Gaussian noise initialization aligns noise across frames.
• Pre- and post-attention injections, using projected features from randomly chosen keyframes, enforce sharp temporal consistency and cross-frame semantic alignment.
• Classifier-free guidance combines conditional and unconditional denoiser predictions, with a tunable scale $w>1$, to regulate adherence to the motion prior implied by video features.

These mechanisms allow explicit control over appearance, motion, and even camera trajectory, supporting both mesh-to-video synthesis and mesh animation driven by external video priors (Cai et al., 2023, Gu et al., 7 Jan 2025).

4. Diffusion Modeling and Deformation Synthesis

The diffusion model operates in the latent space of deformation tokens or point cloud trajectories:

• Forward noising follows standard DDPM (or EDM) schedules: $$z_t = \sqrt{\alpha_t}z_0 + \sqrt{1-\alpha_t}\epsilon$$, $\epsilon \sim \mathcal N(0,I)$.
• Reverse denoising uses a transformer to predict $\mu_\theta(z_t,t,P_1,\mathcal V)$, guiding latent recovery toward clean deformations, steered by mesh and video features.
• Training objectives minimize the L2 error between sampled noise and denoiser predictions.

Following denoising, VAE decoders “unpack” latent sets into deformed point clouds $\widehat P_t$; small MLPs rasterize these into mesh vertex displacements $\Delta V_t$, yielding $V_t = V_0 + \Delta V_t$ (Shi et al., 9 Jun 2025). Some frameworks (e.g., MotionDreamer (Uzolas et al., 2024), AnimaMimic (Xie et al., 16 Dec 2025)) incorporate explicit mesh parameterization via blend weights, skinning, or per-triangle Jacobians; differentiable rendering and simulation modules ensure physically plausible, artist-editable results.

5. Temporal Consistency and Correspondence Injection

Temporal coherence is enforced by using correspondence information (e.g., UV maps, tracked keypoints, pose maps) and well-posed noise initialization. Feature blending in UV-space ensures that identical surface points are treated consistently across the sequence, reducing texture-sticking and cross-frame smearing (Cai et al., 2023). Ablation studies consistently demonstrate that dropping pre- or post-attention injection, using frame-specific instead of UV-initialized noise, or omitting latent normalization leads to artifact-prone or temporally unstable outputs.

In skeletal or pose-based animation frameworks (e.g., AnimaX (Huang et al., 24 Jun 2025)), multi-view pose maps and shared positional encodings guarantee spatial–temporal alignment; 2D-to-3D joint triangulation followed by inverse kinematics recovers physically plausible mesh motion.

6. Quantitative Evaluation and Comparative Analysis

Empirical validation relies on both objective and subjective metrics. DriveAnyMesh achieves state-of-the-art appearance and geometry results against four prior 4D mesh animation baselines (e.g., PSNR 24.39, SSIM 0.950, LPIPS 0.030, Chamfer distance 0.018) (Shi et al., 9 Jun 2025). Generative Rendering yields the highest frame-to-frame CLIP similarity (0.9845) among video-guided mesh animation methods (Cai et al., 2023). MotionDreamer and AnimaX report top scores on human benchmark datasets and broad generalization across mesh classes (Uzolas et al., 2024, Huang et al., 24 Jun 2025). Large user studies consistently prefer outputs from video diffusion–guided mesh animation pipelines on realism, temporal consistency, motion plausibility, and prompt adherence.

7. Future Directions and Limitations

Although these frameworks have advanced the fidelity and control of mesh animation, open challenges remain:

• Canonical mesh misalignment and pose errors in two-stage models (e.g., GS generators in (Zhang et al., 31 Jul 2025)).
• Artifacts caused by video model morphing, monocular ambiguity, and failure of correspondence mapping under rapid motion or occlusion (Millán et al., 20 Mar 2025).
• Potential for end-to-end 4D diffusion, joint canonical mesh and variation generation, and autoregressive long-sequence synthesis.
• Expanding physical realism via differentiable simulation modules and physics-based refinement, as exemplified in AnimaMimic (Xie et al., 16 Dec 2025) and PhysAnimator (Xie et al., 27 Jan 2025).

A plausible implication is that integration of cross-modal diffusion priors, explicit mesh correspondence channels, and robust physical models will further extend the scope and aesthetic quality of video-driven mesh animation, with significant relevance for entertainment, simulation, and immersive applications.