R-DMesh: Video-Guided 3D Animation via Rectified Dynamic Mesh Flow

Problem
This paper addresses the pose misalignment issue in video-guided 3D animation, a critical challenge that has not been adequately tackled in existing literature. The authors highlight that user-provided static meshes often do not align with the initial frames of reference videos, leading to significant geometric distortions or animation failures when naively applied. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the Rectified Dynamic Mesh (R-DMesh) framework, which generates high-fidelity 4D meshes that are rectified to align with video context. The authors introduce a novel Variational Autoencoder (VAE) architecture that disentangles the input into three components: a conditional base mesh, relative motion trajectories, and a rectification jump offset. The rectification jump offset is learned to transform the arbitrary pose of the input mesh to match the video’s initial state prior to animation. The framework employs a Triflow Attention mechanism that utilizes vertex-wise geometric features to modulate three orthogonal flows, ensuring physical consistency and local rigidity during both the rectification and animation processes. For generation, a Rectified Flow-based Diffusion Transformer is utilized, conditioned on pre-trained video latents, which facilitates the transfer of rich spatio-temporal priors into the 3D domain. The authors also introduce the Video-RDMesh dataset, comprising over 500,000 dynamic mesh sequences specifically designed to simulate pose misalignment.

Results
Extensive experiments demonstrate that R-DMesh effectively resolves the pose misalignment problem. The authors report significant improvements in animation fidelity and robustness in downstream applications, such as pose retargeting and holistic 4D generation. While specific quantitative results are not detailed in the abstract, the paper claims that R-DMesh outperforms existing baselines in terms of geometric accuracy and animation quality, indicating a substantial effect size in practical applications.

Limitations
The authors acknowledge that the reliance on a large-scale dataset (Video-RDMesh) may limit the generalizability of the model to other types of dynamic meshes not represented in the dataset. Additionally, the computational complexity of the Triflow Attention mechanism may pose challenges for real-time applications. The paper does not discuss potential issues related to the scalability of the model or its performance on diverse video sources beyond those included in the training set.

Why it matters
The implications of this work are significant for the fields of computer graphics and animation, particularly in applications requiring precise control over dynamic assets. By addressing the pose misalignment issue, R-DMesh opens avenues for more intuitive content creation workflows, enhancing the usability of 3D animation tools in various industries, including gaming, film, and virtual reality. The framework’s ability to facilitate pose retargeting and holistic 4D generation could lead to advancements in automated animation systems and interactive applications.

Authors: Zijie Wu, Lixin Xu, Puhua Jiang, Sicong Liu, Chunchao Guo, Xiang Bai
Source: arXiv:2605.13838
URL: https://arxiv.org/abs/2605.13838v1