Animate3D: Animating Any 3D Model with Multi-view Video Diffusion (2407.11398v2)

Abstract: Recent advances in 4D generation mainly focus on generating 4D content by distilling pre-trained text or single-view image-conditioned models. It is inconvenient for them to take advantage of various off-the-shelf 3D assets with multi-view attributes, and their results suffer from spatiotemporal inconsistency owing to the inherent ambiguity in the supervision signals. In this work, we present Animate3D, a novel framework for animating any static 3D model. The core idea is two-fold: 1) We propose a novel multi-view video diffusion model (MV-VDM) conditioned on multi-view renderings of the static 3D object, which is trained on our presented large-scale multi-view video dataset (MV-Video). 2) Based on MV-VDM, we introduce a framework combining reconstruction and 4D Score Distillation Sampling (4D-SDS) to leverage the multi-view video diffusion priors for animating 3D objects. Specifically, for MV-VDM, we design a new spatiotemporal attention module to enhance spatial and temporal consistency by integrating 3D and video diffusion models. Additionally, we leverage the static 3D model's multi-view renderings as conditions to preserve its identity. For animating 3D models, an effective two-stage pipeline is proposed: we first reconstruct motions directly from generated multi-view videos, followed by the introduced 4D-SDS to refine both appearance and motion. Benefiting from accurate motion learning, we could achieve straightforward mesh animation. Qualitative and quantitative experiments demonstrate that Animate3D significantly outperforms previous approaches. Data, code, and models will be open-released.

An Overview of Animate3D: Animating Any 3D Model with Multi-view Video Diffusion

The paper "Animate3D: Animating Any 3D Model with Multi-view Video Diffusion" presents a novel framework, Animate3D, for animating static 3D models. The primary innovation in this work is the introduction of the Multi-view Video Diffusion Model (MV-VDM) that leverages multi-view renderings of static 3D objects to generate dynamic animations with enhanced spatiotemporal consistency.

Key Contributions

1. Multi-view Video Diffusion Model (MV-VDM): The authors propose MV-VDM, conditioned on multi-view images of static 3D models, capable of producing coherent multi-view videos. The uniqueness of MV-VDM lies in its integration of spatiotemporal attention mechanisms and its foundation on pre-trained 3D and video diffusion models, allowing it to preserve object identity while achieving dynamic and consistent animations.
2. Robust Training Dataset: A significant contribution is the assembly of a large-scale dataset, termed MV-Video, consisting of approximately 115,566 animations rendered from 53,340 animated 3D models. This dataset underpins the training of MV-VDM, providing diverse and high-quality training examples essential for mastering the intricacies of 4D generation.
3. Efficient Animation Framework: The Animate3D framework offers a two-stage pipeline for animating static 3D models. The pipeline first uses motion reconstruction directly from generated multi-view videos, followed by a 4D Score Distillation Sampling (4D-SDS) to enhance both the appearance and the motion details of the animation.
4. 4D Gaussian Splatting Optimization: The paper utilizes 4D Gaussian Splatting (4DGS) for representing dynamic scenes with efficiency, offering advantages in maintaining high-quality dynamics and appearance throughout the animation process.

Experimental Evaluation

Comprehensive qualitative and quantitative evaluations demonstrate that Animate3D significantly surpasses existing methods in terms of visual and motion fidelity. Key metrics such as I2V Subject, Motion Smoothness, Dynamic Degree, and Aesthetic Quality show improved scores, validating the efficacy of the proposed spatiotemporal attention mechanisms and 4D-SDS optimization.

Practical and Theoretical Implications

The paper suggests several practical implications:

• Enhanced 3D Content Creation: Industries such as AR/VR, gaming, and digital media may greatly benefit from such advancements, offering more intuitive and automated tools for content creation.
• Integration with Pre-trained Models: By leveraging existing pre-trained models, Animate3D underscores the feasibility of transferring learning across different modalities (images, videos, 3D models), which may catalyze further multi-modality research in AI.

On a theoretical level, the framework promotes a paradigm that integrates static and dynamic modeling techniques, potentially influencing future methodologies in spatiotemporal data processing.

Outlook and Future Directions

Future work could focus on refining the pipeline to reduce processing time, enhancing real-world applicability by bridging the domain gap between synthetic and real-world data. Additionally, developing new evaluation metrics tailored to 4D generation tasks remains an important area for ongoing research.

In conclusion, Animate3D represents a significant step forward in animating static 3D models with enhanced fidelity and cross-modal integration, paving the way for more advanced and practical applications in dynamic 3D content generation.