Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Authors: Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our work... Datasets We chose three multi-view video datasets... We compare our method to state-of-the-art non-articulated and articulated methods... Novel view synthesis We provide results for the Robots view synthesis without the skeleton simplification; quantitatively in Fig. 4 and qualitatively in Fig. 3. Loss ablation Our experiments suggest the regularization does not improve the image quality, but it improves the quality of the kinematic model, which is important for our main goal of reposing (see Fig. 9). |
| Researcher Affiliation | Academia | Lukas Uzolas Elmar Eisemann Petr Kellnhofer Delft University of Technology The Netherlands {l.uzolas, e.eisemann, p.kellnhofer}@tudelft.nl |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The project website can be found at https://lukas.uzolas.com/Articulated-Point-Ne RF/. This link points to a project website/demonstration page, not an explicit code repository or statement about open-source code availability for the described methodology. |
| Open Datasets | Yes | Datasets We chose three multi-view video datasets that are commonly used for the evaluation of dynamic multi-view synthesis methods and pose articulation. First, the Robots dataset [20]... Second, the Blender dataset [3]... Third, ZJU-Mo Cap dataset [82]... |
| Dataset Splits | Yes | We use 18 views for training and 2 for evaluation. We use the original training-test split. We use the original training-test split. We use the original training-test split. We use the original training-test split. Finally, we evaluate image quality using peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [114], and learned perceptual image patch similarity (LPIPS) [115] image metrics. |
| Hardware Specification | Yes | All experiments were done on a single Nvidia GPU RTX 3090Ti. |
| Software Dependencies | No | The paper mentions "implemented in Pytorch" and using "Ti Neu Vox [11] backbone using the authors implementation", but no specific version numbers for these software dependencies are provided. |
| Experiment Setup | Yes | We train each scene using the Adam optimizer for 160k (Blender and Robots) or 320k (ZJU-Mo Cap) iterations with a batch size of 8192 rays, sampled randomly from multiple views and a single timestamp. In total, our training loss is L = ω0Lphoto + ω1Lmask + ω2Lskel + ω3Ltranf + ω4Lsmooth + ω5Lsparse + ω6LARAP where ω = {200, 0.02, 1, 0.1, 10, 0.2, 0.005} in our experiments. |