TC4D: Trajectory-Conditioned Text-to-4D Generation (2403.17920v3)

Abstract: Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision from pre-trained text-to-video models. However, existing representations for motion, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate-they cannot synthesize motion extending far beyond the bounding box used for volume rendering. The lack of a more flexible motion model contributes to the gap in realism between 4D generation methods and recent, near-photorealistic video generation models. Here, we propose TC4D: trajectory-conditioned text-to-4D generation, which factors motion into global and local components. We represent the global motion of a scene's bounding box using rigid transformation along a trajectory parameterized by a spline. We learn local deformations that conform to the global trajectory using supervision from a text-to-video model. Our approach enables the synthesis of scenes animated along arbitrary trajectories, compositional scene generation, and significant improvements to the realism and amount of generated motion, which we evaluate qualitatively and through a user study. Video results can be viewed on our website: https://sherwinbahmani.github.io/tc4d.

TC4D: Trajectory-Conditioned Text-to-4D Generation

The paper "TC4D: Trajectory-Conditioned Text-to-4D Generation" presents a novel approach to synthesizing dynamic 3D scenes from textual descriptions. Unlike existing models that are limited to represent small-scale motions confined within a bounding box, TC4D factors motion into global and local components. This methodology addresses the realism gap between current 4D generation techniques and recent state-of-the-art video generation models by decoupling 4D motion into global trajectory-based and local deformation-based movements.

Key Contributions

1. Motion Decomposition:
   • Global Motion: Represented using rigid transformations. Essentially, the bounding box of the scene is animated along a trajectory parameterized by a spline.
   • Local Motion: Modeled through a deformation field that conforms to the global trajectory using Video Score Distillation Sampling (VSDS).
2. Trajectory-Aware VSDS:
   • This novel adaptation of the conventional VSDS allows the model to decouple temporal resolution and duration of generated 4D scenes from the video's temporal constraints. The trajectory is segmented into parts and the learning of local motion is conditioned on these segments.
3. Smoothness and Temporal Coherence:
   • To mitigate high-frequency jitters, the authors introduce a smoothness penalty and a novel annealing procedure for diffusion time steps during optimization.
4. Evaluation and Results:
   • Extensive user studies are performed to evaluate the quality of the generated 4D content in comparison with baseline methods.
   • Statistically significant improvements are demonstrated in both the amount of motion and its realism.

Methodology

The approach introduces a factorized motion model where global motion is represented by a trajectory-based rigid transformation and local motion by a learned deformation field optimized using a text-to-video model. By segmenting the trajectory, the method ensures the video model supervises appropriately over long sequences, something that was challenging with prior approaches due to their frame limitations.

Furthermore, the implementation uses hash-encoded feature grids to encode both 3D neural radiance fields and the deformation fields, and a combination of pre-trained 3D-aware text-to-image models and video diffusion models to initialize and supervise the learning process.

Quantitative and Qualitative Evaluation

The authors conducted user studies comprising 20 evaluators to compare TC4D with baseline methods like a modified version of 4D-fy. Metrics included appearance quality (AQ), structure quality (SQ), motion quality (MQ), motion amount (MA), and text alignment (TA). Results indicate a substantial preference for TC4D in terms of both motion quality and amount, underscoring the efficacy of the trajectory-aware approach.

Implications and Future Work

This paper sets a benchmark for future research in the field of text-to-4D generation. By successfully decoupling the generation of motion into global and local components, it opens up several avenues for further exploration:

1. Automated Trajectory Generation:
   • Integrating trajectory generation into the synthesis pipeline could allow for fully automated text-to-4D generation.
   • Optimization or generative models for scene layouts and trajectories may further streamline the process.
2. Multi-Object and Interaction Modeling:
   • Extending the model to handle multiple interacting objects could significantly enhance the range of possible 4D scenes.
   • Real-world applications like virtual reality and industrial design could benefit from more sophisticated motion models.
3. Large-Unbounded Scenes:
   • An exciting direction would be to expand the approach to unbounded scenes, improving scalability and applicability to vast environments.

Conclusion

The TC4D model marks a significant advancement in the synthesis of dynamic 3D scenes by addressing key limitations of prior 4D generation techniques. This novel framework demonstrates the potential for generating realistic, large-scale motions in 4D scenes, driven by textual descriptions. With meaningful improvements laid out, this paves the way for further innovations that can enhance the quality and applicability of AI-generated 4D content in various domains.