This content originally appeared on HackerNoon and was authored by The FewShot Prompting Publication
Table of Links
3. Method and 3.1. Architecture
3.2. Loss and 3.3. Implementation Details
4. Data Curation
5. Experiments and 5.1. Metrics
5.3. Comparison to SOTA Methods
5.4. Qualitative Results and 5.5. Ablation Study
\ A. Additional Qualitative Comparison
B. Inference on AI-generated Images
B. Inference on AI-generated Images
We present additional results of ZeroShape using images generated with DALL·E 3. To test the out-of-domain generalization ability, we generate images of imaginary objects as the input to our model (see Fig. 10). Despite the domain gap to realistic or rendered images, ZeroShape can faithfully recover the global shape structure and accurately follow the local geometry cues from the input image. These results also demonstrate the potential of using ZeroShape in a text-based 3D generation workflow.
\
:::info This paper is available on arxiv under CC BY 4.0 DEED license.
:::
:::info 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.
:::
\
This content originally appeared on HackerNoon and was authored by The FewShot Prompting Publication
The FewShot Prompting Publication | Sciencx (2025-01-03T16:15:03+00:00) ZeroShape: The Inference on AI-Generated Images. Retrieved from https://www.scien.cx/2025/01/03/zeroshape-the-inference-on-ai-generated-images/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.