DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative. While human pose estimation and tracking has seen rapid progress thanks to large annotated datasets, animal pose remain challenging, due to large morphological and behavioural differences between species and limited annotated data.
arXiv:2512.07776v2 Announce Type: replace Abstract: Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals fr...
VastMAT is a large‑scale multi‑animal tracking benchmark featuring 2,947 videos, 337 animal categories, and over 3.6 million bounding boxes with 22,883 identity trajectories. It emphasizes high‑quality, expert‑reviewed annotations and introduces Seen‑category and Unseen‑category evaluation protocols, revealing significant challenges in tracking unseen animals. The authors also propose a lightweight Center‑Distance‑Augmented Association module that boosts HOTA scores for existing MOT methods without extra training.
SDPose leverages pre‑trained diffusion models, specifically the Stable Diffusion U‑Net, to extract robust latent features for human pose estimation. By identifying the most effective upsampling levels, fusing these features with a lightweight aggregation module, and jointly training with keypoint heatmap supervision and a latent reconstruction loss, SDPose achieves strong cross‑domain generalization. The authors introduce the COCO‑OOD benchmark, comprising style‑transferred and corrupted subsets, to evaluate robustness, and demonstrate that SDPose matches Sapiens on COCO while outperforming competitors in zero‑shot cross‑domain scenarios.
The paper introduces a new approach to 3D object pose estimation that leverages diffusion model features, such as those from Stable Diffusion, to improve performance on unseen objects. Three aggregation architectures—vanilla, nonlinear, and context‑aware—are proposed, with a confidence‑adaptive aggregation network that selectively uses discriminative features to balance speed and accuracy. The method achieves state‑of‑the‑art accuracy on unseen object benchmarks (97.7% vs. 93.5% on Unseen LM) and real‑time performance (68 FPS) while reducing computational cost.
The paper presents a generative framework that estimates category-level 6D pose and 3D size of objects from a single RGB image, using score-based diffusion models to produce a multi-hypothesis pose distribution. It replaces costly likelihood pruning with a Mean Shift approach to isolate the mode as the final pose estimate, achieving state-of-the-art results on the REAL275 benchmark. The method also decouples detection from pose estimation, enabling robust zero-shot generalisation on the Wild6D dataset and extending naturally to video sequences by propagating the pose distribution over time.