DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation (2410.06756v1)

Abstract: Recent advancements in 2D/3D generative techniques have facilitated the generation of dynamic 3D objects from monocular videos. Previous methods mainly rely on the implicit neural radiance fields (NeRF) or explicit Gaussian Splatting as the underlying representation, and struggle to achieve satisfactory spatial-temporal consistency and surface appearance. Drawing inspiration from modern 3D animation pipelines, we introduce DreamMesh4D, a novel framework combining mesh representation with geometric skinning technique to generate high-quality 4D object from a monocular video. Instead of utilizing classical texture map for appearance, we bind Gaussian splats to triangle face of mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh obtained through an image-to-3D generation procedure. Sparse points are then uniformly sampled across the mesh surface, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the surface Gaussians are deformed via a novel geometric skinning algorithm, which is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the deformation network are learned via reference view photometric loss, score distillation loss as well as other regularizers in a two-stage manner. Extensive experiments demonstrate superior performance of our method. Furthermore, our method is compatible with modern graphic pipelines, showcasing its potential in the 3D gaming and film industry.

• The paper introduces a hybrid representation that fuses mesh geometry with Gaussian splatting to optimize the generation of dynamic 4D objects.
• It employs an adaptive skinning algorithm that blends LBS and DQS to overcome common issues like volume loss and joint-bulging artifacts.
• Extensive experiments demonstrate significant improvements in rendering quality and temporal consistency, validated by metrics such as PSNR and FVD.

DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation

The DreamMesh4D framework represents a pivotal stride in video-to-4D generation by leveraging a synergistic combination of mesh representation and Gaussian splatting techniques. This paper introduces a novel approach aimed at enhancing the spatial-temporal consistency and surface appearance of dynamic 4D objects constructed from monocular videos.

Key Contributions

DreamMesh4D stands out by integrating mesh representations with geometric skinning techniques, diverging from traditional methods reliant on either implicit neural radiance fields (NeRF) or explicit Gaussian splatting. By augmenting the triangular faces of the mesh with Gaussian splats, the framework facilitates differentiable optimization of textures and mesh vertices. This hybrid representation, inspired by recent advances in 3D animation pipelines, marries the benefits of explicit surface modelling with the fidelity of detail offered by Gaussian-based appearance techniques.

Methodological Insights

The architecture of DreamMesh4D comprises two primary stages. Initially, a coarse mesh generated from an image-to-3D pipeline undergoes refinement through the binding of surface Gaussians. Sparse control points across the mesh facilitate the crafting of a deformation graph, driving object motion and optimizing computational efficiency.

The highlight of this research is the introduction of an adaptive hybrid skinning algorithm, which cleverly combines linear blending skinning (LBS) and dual-quaternion skinning (DQS). This approach addresses the well-documented limitations of both LBS (such as volume loss) and DQS (such as joint-bulging artifacts), thereby optimizing real-time object deformation by ensuring spatial-temporal coherence.

Experimental Validation

The paper provides extensive experimental results demonstrating the framework's superiority in producing high-fidelity dynamic meshes. The method significantly outperforms existing baselines regarding rendering quality and spatial-temporal consistency. Additionally, the compatibility of DreamMesh4D with modern graphic pipelines underscores its potential utility in fields such as 3D gaming and the film industry.

Quantitative comparisons reveal that DreamMesh4D achieves notable improvements in metrics such as PSNR and FVD, indicative of its enhanced visual and temporal coherence capabilities. These outcomes underscore the efficacy of the hybrid representation and the novel geometric skinning approach.

Discussion and Future Directions

The implications of the DreamMesh4D framework are substantial, offering possibilities for applications across augmented reality (AR), virtual reality (VR), and cinematic content creation. The hybrid Gaussian-mesh representation, coupled with the adaptive skinning technique, emphasizes the potential for more seamless integration of artificial intelligence with existing content production workflows.

Future research can build upon this work by addressing the current limitations, such as optimization time constraints and the focus on object-level generation. Expanding the framework to accommodate dynamic scenes with moving cameras represents a promising direction. Additionally, enhancing the performance of the employed multi-view diffusion models for broader applicability could further amplify the utility of the framework.

In sum, DreamMesh4D exemplifies a robust methodology in 4D content generation, marking a significant contribution to the field by aligning AI advancements with practical industry needs.