PointInfinity: Resolution-Invariant Point Diffusion Models
Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-Yuan Wu
2024年份
10顶会引用
摘要
1k 4k 16k 131k Surface Image Figure 1. We present a resolution-invariant point cloud diffusion model that trains at low-resolution (down to 64 points), but generates high-resolution point clouds (up to 131k points). This test-time resolution scaling improves our generation quality. We visualize our high-resolution 131k point clouds by converting them to a continuous surface.
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引用它的顶会 Paper10
- Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded ImagesDonghwan Kim, Tae-Kyun KimNeurIPS 2024 · 被引用 8 次
- Anymate: A Dataset and Baselines for Learning 3D Object RiggingYufan Deng, Yuhao Zhang, Chen Geng, Shangzhe Wu 等SIGGRAPH 2025 · 被引用 5 次
- Textured 3D Regenerative Morphing with 3D Diffusion PriorSonglin Yang, Yushi Lan, Honghua Chen, Xingang PanICCV 2025 · 被引用 1 次
- SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single ImagesZixuan Huang, Mark Boss, Aaryaman Vasishta, James M. Rehg 等CVPR 2025
- Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface RepresentationSlava Elizarov, Ciara Rowles, Simon DonnéICLR 2025
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