BEAST3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting
- URL: http://arxiv.org/abs/2606.02937v1
- Date: Mon, 01 Jun 2026 22:34:14 GMT
- Title: BEAST3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting
- Abstract summary: BEAST3D is a self-supervised pretraining framework that learns 3D visual representations from unlabeled, calibrated multi-view video.<n>BEAST3D reconstructs 3D structure with as few as four views by conditioning directly on known camera parameters.
- Score: 6.870390707295292
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-view video recordings are increasingly used to capture the 3D movements of animals in experimental settings, yet extracting rich 3D representations from these recordings remains challenging. Supervised pose estimation requires extensive manual annotation, while general-purpose 3D reconstruction models trained on generic scene datasets fail on the specialized imagery and sparse-view setting of laboratory experiments. We address these limitations with BEAST3D, a self-supervised pretraining framework that learns 3D visual representations from unlabeled, calibrated multi-view video. BEAST3D uses a vision transformer to predict 3D Gaussian splats that reconstruct held-out views through differentiable rendering, while simultaneously segmenting the animal from the background. BEAST3D reconstructs 3D structure with as few as four views by conditioning directly on known camera parameters--unlike general-purpose models, which must estimate camera geometry from dense overlapping viewpoints that are seldom available in lab settings. Through comprehensive evaluation across four species, we demonstrate that BEAST3D produces rich, viewpoint-invariant features that transfer effectively to three downstream tasks: novel view synthesis, which validates the quality of the learned 3D representations; multi-view pose estimation, which provides the sparse keypoint trajectories widely used in behavioral analysis; and neural encoding, which relates 3D behavioral features to simultaneously recorded neural activity. BEAST3D thus establishes a versatile framework for behavioral analysis that leverages 3D structure in modern multi-view laboratory recordings.
Related papers
- Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis [38.10984626023432]
We introduce a novel benchmark for in-context 3D scene understanding that requires no finetuning and directly probes the quality of dense visual features.<n>We benchmark 8 state-of-the-art foundation models and show DINO-based encoders remain competitive across large viewpoint shifts.
arXiv Detail & Related papers (2025-12-12T14:03:16Z) - PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos [25.79551555341372]
PAD3R is a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos.<n>At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model.<n>By combining generative priors and differentiable rendering, PAD3R reconstructs high-fidelity, articulated 3D representations of objects in a category-agnostic way.
arXiv Detail & Related papers (2025-09-29T17:59:33Z) - 4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos [15.063635374924209]
We propose 4D-Animal, a novel framework that reconstructs animatable 3D animals from videos without requiring sparse keypoint annotations.<n>Our approach introduces a dense feature network that maps 2D representations to SMAL parameters, enhancing both the efficiency and stability of the fitting process.
arXiv Detail & Related papers (2025-07-14T16:24:31Z) - UVRM: A Scalable 3D Reconstruction Model from Unposed Videos [68.34221167200259]
Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples.<n>We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose.
arXiv Detail & Related papers (2025-01-16T08:00:17Z) - Virtual Pets: Animatable Animal Generation in 3D Scenes [84.0990909455833]
We introduce Virtual Pet, a novel pipeline to model realistic and diverse motions for target animal species within a 3D environment.
We leverage monocular internet videos and extract deformable NeRF representations for the foreground and static NeRF representations for the background.
We develop a reconstruction strategy, encompassing species-level shared template learning and per-video fine-tuning.
arXiv Detail & Related papers (2023-12-21T18:59:30Z) - AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
The 3D autodecoder framework embeds properties learned from the target dataset in the latent space.
We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
arXiv Detail & Related papers (2023-07-07T17:59:14Z) - Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable
Categories [80.30216777363057]
We introduce Common Pets in 3D (CoP3D), a collection of crowd-sourced videos showing around 4,200 distinct pets.
At test time, given a small number of video frames of an unseen object, Tracker-NeRF predicts the trajectories of its 3D points and generates new views.
Results on CoP3D reveal significantly better non-rigid new-view synthesis performance than existing baselines.
arXiv Detail & Related papers (2022-11-07T22:42:42Z) - Spatio-temporal Self-Supervised Representation Learning for 3D Point
Clouds [96.9027094562957]
We introduce a-temporal representation learning framework, capable of learning from unlabeled tasks.
Inspired by how infants learn from visual data in the wild, we explore rich cues derived from the 3D data.
STRL takes two temporally-related frames from a 3D point cloud sequence as the input, transforms it with the spatial data augmentation, and learns the invariant representation self-supervisedly.
arXiv Detail & Related papers (2021-09-01T04:17:11Z) - Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled
Representation [57.11299763566534]
We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points.
Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
arXiv Detail & Related papers (2020-04-05T12:52:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.