Table of Links
Abstract and 1 Introduction
2. Related Work
3. Method and 3.1. Architecture
3.2. Loss and 3.3. Implementation Details
4. Data Curation
4.1. Training Dataset
4.2. Evaluation Benchmark
5. Experiments and 5.1. Metrics
5.2. Baselines
5.3. Comparison to SOTA Methods
5.4. Qualitative Results and 5.5. Ablation Study
6. Limitations and Discussion
7. Conclusion and References
A. Additional Qualitative Comparison
B. Inference on AI-generated Images
C. Data Curation Details
A. Additional Qualitative Comparison
We show additional qualitative results on OmniObject3D, Ocrtoc3D and Pix3D in Fig. 7, Fig. 8 and Fig. 9, respectively. Comparing with prior arts, the reconstruction of ZeroShape better captures the global shape structure and visible geometric details.
This paper is available on arxiv under CC BY 4.0 DEED license.
Authors:
(1) Zixuan Huang, University of Illinois at Urbana-Champaign and both authors contributed equally to this work;
(2) Stefan Stojanov, Georgia Institute of Technology and both authors contributed equally to this work;
(3) Anh Thai, Georgia Institute of Technology;
(4) Varun Jampani, Stability AI;
(5) James M. Rehg, University of Illinois at Urbana-Champaign.
