TRANS4D: Realistic Geometry-Aware Transitions for Compositional Text-to-4D Synthesis

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Recent advances in diffusion models have greatly improved the ability to synthesize 4D objects and scenes. Existing methods can generate high-quality results based on user-friendly conditions, but they often struggle to capture complex object deformations or interactions. A new paper introduces TRANS4D, a text-to-4D synthesis framework that enables realistic complex scene transitions by using multi-modal language models (MLLMs) for physics-aware scene planning and a geometry-aware transition network to simulate object deformation.

TRANS4D leverages MLLMs to generate detailed physical information such as initial positions, movement and rotation speeds, and transition times, enabling more precise scene initialization and transition management. This allows the framework to handle large-scale object transformations. The proposed Transition Network predicts whether each point in the 3D scene model should appear or disappear at a specific time, ensuring a smooth and natural 4D scene transition.

For instance, consider the text prompt “The missile collided with the plane and exploded”. TRANS4D can generate a scene where the missile moves towards the plane, collides with it, and then transforms into an exploding cloud of smoke. This scenario is difficult for existing methods as it requires complex object deformation and global interactions between multiple objects.

The paper presents extensive experiments demonstrating that TRANS4D consistently outperforms existing methods in generating 4D scenes with accurate and high-quality transitions, validating its effectiveness.

The authors highlight the following key contributions of TRANS4D:

• Physics-aware 4D Transition Planning: The framework leverages MLLMs to predict detailed physical information about the scene, ensuring a more realistic and coherent 4D scene synthesis.
• Geometry-aware Transition Network: This network allows the framework to seamlessly simulate significant object deformations, such as explosions or transformations.
• Efficient 4D Training and Refinement: The training process efficiently optimizes the model for high-fidelity 4D scene synthesis by using two-stage training: a first stage that trains the deformation network and Transition Network using a small number of points and a second stage that refines the 3D scene models using a larger number of points, which leads to higher point cloud counts and lower computational overhead.

The paper demonstrates that TRANS4D represents a significant advancement in 4D scene generation, paving the way for more sophisticated applications in gaming and video industries. This work highlights the potential of using MLLMs for scene planning and a geometry-aware transition network for creating high-quality 4D scenes with complex interactions and significant object deformations.

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