Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation
Authors: Tserendorj Adiya, Jae Shin Yoon, JUNGEUN LEE, Sanghun Kim, Hwasup Lim
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, our method demonstrates strong performance compared to existing unidirectional approaches with realistic temporal coherence. We validate our bidirectional temporal diffusion model on two tasks: |
| Researcher Affiliation | Collaboration | Tserendorj Adiya3, Jae Shin Yoon2 Jungeun Lee1 Sanghun Kim1 Hwasup Lim1 1Korea Institute of Science and Technology 2Adobe 3AI Center, CJ Corporation |
| Pseudocode | Yes | Algorithm 1 Bidirectional Recursive Sampling Input: Initial noisy inputs Y k = {yk 1, ..., yk t }, driven pose sequence S = {s1, ..., s T }. Output: Denoised animation Y 0 = {y0 1, ..., y0 t } for k = K 1 to 0 step 1 do if K k is odd then Direction: Forward for t = 1 to T do yk 1 t = fθ(yk t , yk t 1, λ(k), st, df) end for else Direction: Backward for t = T to 1 step 1 do yk 1 t 1 = fθ(yk t 1, yk t , λ(k), st 1, db) end for end if end for |
| Open Source Code | No | The paper does not provide a direct link to a code repository or an explicit statement about the release of its source code. |
| Open Datasets | Yes | UBC Fashion dataset Zablotskaia et al. (2019): it consists of 500 training and 100 testing videos of individuals wearing various outfits and rotating 360 degrees. Each video lasts approximately 12 seconds at 30 FPS. |
| Dataset Splits | Yes | The dataset includes a total of 80 training videos and 19 testing videos, each of which lasts 32 seconds at 30 FPS. |
| Hardware Specification | Yes | training the BTU-Net and SR3 models using the UBC fashion dataset requires 15 and 30 epochs, respectively, on a setup of four A100 GPUs, completed within 67 hours. |
| Software Dependencies | No | The paper mentions software tools like Character Creator 4, iClone8, Mixamo motion data, and Nvidia Omniverse, as well as SR3 (with a citation). However, it does not provide specific version numbers for these or other programming libraries/frameworks (e.g., Python, PyTorch, CUDA) required for reproducibility. |
| Experiment Setup | Yes | Our method is trained at a resolution of 256x256... for 50k and 100k iterations with a batch size of 32, respectively. We set the denoising step to K = 1000 and the learning rate to 1e-5. During testing, we fine-tune model with test appearance condition for 300 iterations with a learning rate of 1e-5. It should be noted that we employ K = 50 at test time for expedited generation. |