Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency (2503.20785v1)

Abstract: We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multiview videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.

• The paper introduces a tuning-free framework that generates dynamic 4D scenes from a single input image using pre-trained diffusion models.
• It employs adaptive guidance with point-guided denoising and latent replacement to ensure spatial and temporal consistency across multi-view videos.
• The approach refines initial multi-view outputs into a coherent 4D representation, enabling efficient real-time, controllable rendering without extensive training.

Free4D (2503.20785) presents a framework for generating dynamic 4D scenes from a single input image without requiring any model fine-tuning. This approach addresses limitations of prior work, which often focused solely on object-level generation or necessitated extensive training on scarce multi-view video datasets. The core innovation of Free4D lies in its leveraging of pre-trained foundation models (specifically image-to-video diffusion models and potentially depth estimators) to synthesize spatially and temporally consistent multi-view videos, which are then lifted into a coherent 4D representation. This tuning-free methodology enhances efficiency and generalizability.

Methodology Overview

The Free4D pipeline comprises three sequential stages designed to transform a single static image into a dynamic 4D scene representation suitable for real-time, controllable rendering:

1. Image Animation and Structure Initialization: The process begins by animating the input static image using a pre-trained image-to-video (I2V) diffusion model. Concurrently, an initial coarse 4D geometric structure is established, likely leveraging monocular depth estimation techniques followed by projecting pixels into a 3D point cloud that evolves over the short temporal sequence generated by the I2V model.
2. Consistent Multi-View Video Generation: This stage aims to convert the initial coarse structure and single-view video into a set of spatially and temporally consistent multi-view videos. It employs a novel adaptive guidance mechanism incorporating two key strategies:
   • Point-Guided Denoising: Enforces spatial consistency across different camera viewpoints during the diffusion sampling process.
   • Latent Replacement: Ensures temporal coherence within and across the generated multi-view video sequences.
3. 4D Representation Refinement: The final stage lifts the generated multi-view video observations into a refined, consistent 4D representation. A modulation-based refinement technique is introduced to mitigate inconsistencies inherited from the previous stage while maximizing the utilization of information contained within the generated multi-view data.

Stage 1: Image Animation and Structure Initialization

The initial step involves generating motion from the static input image. An off-the-shelf I2V diffusion model (e.g., models like SVD or AnimateDiff adapted for single-image input) is employed to synthesize a short video sequence depicting plausible motion based on the image content. This provides the initial temporal dynamics. Simultaneously, a coarse 4D geometry is initialized. This likely involves:

1. Estimating depth for the input image using a pre-trained monocular depth estimation network (e.g., MiDaS, DPT).
2. Unprojecting the 2D pixels into a 3D point cloud based on the estimated depth and camera intrinsics (often assumed or estimated).
3. Propagating this initial 3D structure across the short time sequence generated by the I2V model, possibly using simple motion heuristics or optical flow estimations derived from the generated video, resulting in a nascent 4D point cloud ($P_0$).

This initial structure ($P_0$) and the single-view video ($V_0$) serve as the foundation for the subsequent multi-view generation process.

Stage 2: Consistent Multi-View Video Generation

Generating consistent multi-view videos from a single view and coarse geometry is challenging. Free4D addresses this by guiding the denoising process of a diffusion model (potentially the same I2V model or a related text-to-video/image model conditioned appropriately) across multiple target camera viewpoints ($C_1, ..., C_N$) and time steps ($t_1, ..., t_T$). The core is the adaptive guidance mechanism:

Point-Guided Denoising for Spatial Consistency

To ensure that the appearance and geometry are consistent when viewed from different angles at the same time step, a point-guided denoising strategy is used. This likely involves:

1. Projecting the current estimate of the 4D structure (potentially refined iteratively) onto the target views ($C_i$) at a given time step $t_j$.
2. Using these projected 2D points as spatial anchors during the diffusion denoising step for each view $i$ at time $t_j$. The guidance mechanism likely modifies the diffusion model's sampling process (e.g., manipulating intermediate activations or gradients) to encourage the generated pixels around these projected points to align consistently across views, reflecting a coherent underlying 3D structure. This could be implemented by adding a loss term during sampling that penalizes inconsistencies in appearance or geometry implied by the generated views relative to the projected points, guiding the reverse diffusion process.

Latent Replacement for Temporal Coherence

Temporal coherence, ensuring smooth motion and consistent appearance over time within each view and across views, is achieved via a novel latent replacement strategy. Standard video diffusion models can sometimes introduce temporal discontinuities. This strategy likely operates within the latent space of the diffusion model's U-Net architecture during the generation of frame $t_j$ for view $C_i$:

1. It might involve injecting or replacing parts of the latent representation for the current frame ($t_j$) with corresponding latent features from the previously generated frame ($t_{j-1}$) for the same view ($C_i$) or even corresponding frames in other views ($C_k, k \neq i$).
2. This replacement could be applied selectively, perhaps focusing on background regions or regions with expected static appearance, while allowing foreground or dynamic regions to evolve more freely based on the I2V model's motion priors. The mechanism aims to preserve consistency in static areas and enforce smoother transitions in dynamic areas across time and potentially across views by sharing latent information strategically.

These two strategies work in tandem within the diffusion sampling loop to generate a set of multi-view videos ($V_1, ..., V_N$) that exhibit improved spatial and temporal consistency compared to naive independent generation.

Stage 3: 4D Representation Refinement

While Stage 2 improves consistency, imperfections may remain. Stage 3 focuses on refining the final 4D representation by explicitly modeling and mitigating these residual inconsistencies. A modulation-based refinement technique is proposed. The specific 4D representation is not explicitly stated in the abstract but could be based on dynamic Neural Radiance Fields (NeRF) variants or 4D Gaussian Splatting, given the goal of real-time, controllable rendering.

The refinement process likely involves:

1. Initializing the chosen 4D representation (e.g., parameters of a dynamic NeRF or properties of 4D Gaussians) using the generated multi-view videos ($V_1, ..., V_N$) and the associated camera poses ($C_1, ..., C_N$).
2. Optimizing this representation to best reconstruct the generated multi-view videos.
3. The "modulation-based" aspect suggests that the refinement process might incorporate learned modulation signals or biases (perhaps conditioned on time or spatial location) that specifically target and compensate for systematic inconsistencies observed in the generated data. For example, if certain regions consistently exhibit flickering artifacts across views/time, the modulation could learn to suppress these during rendering or optimization. This allows the system to leverage the rich information from the generated videos while actively correcting known artifacts patterns stemming from the diffusion generation process.

The outcome is a refined 4D representation that encodes the scene's geometry, appearance, and dynamics consistently, enabling high-fidelity, real-time rendering from novel viewpoints and potentially at novel times within the learned motion sequence.

Implementation Considerations

• Foundation Models: Relies heavily on pre-trained I2V models (e.g., Stable Video Diffusion) and potentially monocular depth estimators (e.g., MiDaS). The quality of these foundational models directly impacts the final output.
• Tuning-Free: A key advantage is the elimination of per-scene or dataset-specific fine-tuning, reducing data requirements and computational costs associated with training. Inference, however, still involves running large diffusion models multiple times (across views and time steps), which can be computationally intensive, requiring significant VRAM and processing time.
• Guidance Mechanisms: Implementing the point-guided denoising and latent replacement strategies requires modifications to the standard diffusion sampling loop, potentially involving custom sampling procedures or hooks into the diffusion model's internal architecture (e.g., U-Net).
• 4D Representation: The choice of 4D representation (dynamic NeRF, 4D Gaussian Splatting, etc.) impacts storage, optimization time, and rendering performance. 4D Gaussian Splatting, if used, generally offers faster rendering compared to NeRF-based approaches.
• Generalizability vs. Control: While tuning-free enhances generalizability to diverse single images, the control over the generated motion is primarily limited by the capabilities of the underlying I2V model and the initial image content. Fine-grained user control over specific object motions might be limited.
• Consistency Trade-offs: The adaptive guidance and refinement aim to maximize consistency, but residual artifacts or inconsistencies might still occur, especially for complex scenes or motions challenging for the base I2V model.

Conclusion

Free4D (2503.20785) offers a promising tuning-free approach for single-image 4D scene generation by composing pre-trained foundation models with novel guidance and refinement techniques. Its core contributions lie in the adaptive guidance mechanism for generating consistent multi-view videos (using point-guided denoising and latent replacement) and the modulation-based refinement for lifting these observations into a coherent 4D representation. This framework bypasses the need for large-scale 4D datasets and expensive training, potentially enabling broader application of 4D content creation from static images, with outputs suitable for real-time rendering.