CASA: Category-agnostic Skeletal Animal Reconstruction
Authors: Yuefan Wu, Zeyuan Chen, Shaowei Liu, Zhongzheng Ren, Shenlong Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate CASA on both Planet Zoo and the real-world dataset DAVIS [46]. Experiments demonstrate that CASA recovers fine shape and realistic skeleton topology, handles a wide variety of animals, and adapts well to unseen categories. Additionally, we showed that CASA reconstructs a skeletal-animatable character readily compatible with downstream re-animation and simulation tasks. |
| Researcher Affiliation | Academia | Yuefan Wu1 Zeyuan Chen1 Shaowei Liu2 Zhongzheng Ren2 Shenlong Wang2 1 University of Science and Technology of China 2University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper does not include any figure, block, or section explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] we will release the code and data upon acceptance |
| Open Datasets | Yes | To this end, we propose a photo-realistic synthetic dataset Planet Zoo to study the dynamic animal reconstruction problem. |
| Dataset Splits | Yes | For Planet Zoo, we choose 24 out of 249 total animals for testing and use the rest for validation and training. |
| Hardware Specification | Yes | We use eight GTX 2080 Ti and RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions software tools like "Blender" and external libraries like "Point Rend [22]" and "volumetric correspondence net [73]" but does not provide specific version numbers for these or other software dependencies necessary for replication. |
| Experiment Setup | No | The paper states "We use Adam optimizer [21] to learn all the optimization variables. We adopt a scheduling strategy to avoid getting stuck at a local minimum." but does not provide specific experimental setup details such as learning rates, batch sizes, number of epochs, or other concrete hyperparameters in the main text. |