Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach (2502.03639v1)

Abstract: We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling it to track 2D objects with 3D Cartesian coordinates. Building on this, we regularize the shape and motion of objects in the video to eliminate undesired artifacts, \eg, nonphysical deformation. Consequently, we enhance the quality of generated RGB videos and alleviate common issues like object morphing, which are prevalent in current video models due to a lack of shape awareness. With our 3D augmentation and regularization, our model is capable of handling contact-rich scenarios such as task-oriented videos. These videos involve complex interactions of solids, where 3D information is essential for perceiving deformation and contact. Furthermore, our model improves the overall quality of video generation by promoting the 3D consistency of moving objects and reducing abrupt changes in shape and motion.

• The paper introduces the integration of 3D point trajectories into video diffusion models to boost physical realism.
• It leverages the novel PointVid dataset to enhance spatial coherence without the need for complex 3D reconstructions.
• Results demonstrate improved motion smoothness and consistency on benchmarks like VBench and VideoPhy.

Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach

The paper "Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach" presents a novel framework for video generation that aims to enhance the physical realism of generated videos by incorporating 3-dimensional knowledge into the video diffusion models. This approach is underpinned by the introduction of a 3D point trajectory dataset termed as PointVid, which augments traditional 2D video data with spatial information, allowing video models to understand and generate videos with improved 3D consistency.

Methodology Overview

The core innovation in this work is the integration of 3D geometry with video generation processes. This is achieved by augmenting 2D video data with tracked 3D point trajectories. The PointVid dataset provides videos augmented with 3D point clouds that track the movements of objects across various frames. These 3D representations are concise, utilizing pixel space alignment to resolve the inherent 2D limitations of video inputs. This approach sidesteps the need for complex 3D reconstructions, making it computationally efficient yet physically informative.

Training involves a diffusion model fine-tuned on PointVid, which generates both video frames and 3D point representations. Crucially, the training model is structured to align these two modalities, facilitating a learning process where the inherent geometric constraints from the 3D data guide the video generation process. Regularization strategies based on 3D priors are employed to further ensure spatial coherence and alignment, reducing non-physical deformations often observed in traditional video models.

Quantitative and Qualitative Results

Empirical evaluations demonstrate the efficacy of this approach. On benchmarks like VBench and VideoPhy, the proposed method shows substantial improvements in motion smoothness, subject and background consistency, and adherence to physical commonsense. These improvements highlight the model's enhanced ability to generate videos that align well with real-world physical dynamics. Qualitative analyses further illustrate that the method alleviates common artifacts such as object morphing, providing visually coherent and realistic video outputs.

Implications and Future Directions

The integration of 3D data into video generation models represents a significant step toward bridging the gap between visual fidelity and physical realism in generative AI. By emphasizing spatial coherence through 3D point alignment, this approach paves the way for more accurate modeling of object interactions and dynamics, essential for applications in virtual reality, simulation, and beyond.

Future work could explore extending this framework to incorporate more sophisticated geometric representations or incorporating other modalities like temporal consistency checks to further enhance the realism. As the computational overhead associated with higher resolution 3D representations diminishes, this line of research could evolve to provide even richer data for video generative models, potentially extending its reach and applicability across various domains of AI-driven content creation.