Realistic 3D human generation plays a crucial role in many graphics applications. However, current methods still struggle to generate high-quality human geometry and texture while maintaining 3D consistency and inference efficiency. In this work, we address these limitations by introducing TGRHuman, a novel approach for generating realistic 3D humans from text. Our method decouples geometry and texture generation to alleviate the issues commonly encountered in NeRF-based methods. Instead of relying on slow, implicit score-distillation-based optimization, we directly use explicit multi-view observation generation and optimization for efficient 3D synthesis. For geometry generation, we propose a high-resolution generative module for multi-view normals together with a geometry-carving strategy that preserves view consistency and supports loose clothing. For texture generation, we produce spatially consistent RGB observations from densely sampled surrounding views using a carefully designed texture-prior acquisition strategy and a diffusion renderer, enabling detailed human texture synthesis. Experiments show that our method can generate high-quality and consistent 3D human geometry and texture efficiently. TGRHuman outperforms existing text-to-3D human methods in both geometry and texture quality.
Muxin Zhang, Chaohui Yu, Yuanwang Yang et al.· Fundamental Research· 0 citations
Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. We present AnimateAnyMesh++, a feed-forward framework for text-driven animation of arbitrary 3D meshes with substantial upgrades in data, architecture, and generative capability. First, we expand the DyMesh-XL dataset by mining dynamic content from Objaverse-XL, increasing the number of unique identities from 60K to 300K and substantially broadening category and motion diversity. Second, we redesign DyMeshVAE-Flex with power-law topology-aware attention and vertex-normal-enhanced features, which significantly improves trajectory reconstruction, local geometry preservation, and mit igates trajectory-sticking artifacts. Third, we introduce archi tectural changes to both DyMeshVAE-Flex and the rectified flow (RF) generator to support variable-length sequence training and generation, enabling longer animations while preserving reconstruction fidelity. Extensive experiments demonstrate that AnimateAnyMesh++ generates semantically accurate and tem porally coherent mesh animations within seconds, surpassing prior approaches in quality and efficiency. The enlarged DyMesh XL, the upgraded DyMeshVAE-Flex, and variable-length RF to gether deliver consistent gains across benchmarks and in-the-wild meshes. We will release code, models, and the expanded DyMesh XL at https://github.com/JarrentWu1031/AnimateAnyMesh-pp upon acceptance of this manuscript to facilitate research in 4D content creation.
Zijie Wu, Chaohui Yu, Fan Wang et al.· IEEE Transactions on Pattern...· 2 citations· ⚡1
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