SHOW is a mask-promptable framework coupling feed-forward scene reconstruction with human mesh recovery in a unified metric space to resolve scale ambiguity and improve human-scene alignment from monocular video.
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Sam 3d body: Robust full-body human mesh recovery
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2026 25representative citing papers
A transformer model predicts in vivo hip and knee contact forces from uncalibrated monocular video at accuracy matching subject-specific musculoskeletal simulations under leave-one-subject-out validation.
H-Flow learns dense human scene flow from monocular video via joint pose and depth prediction in a multi-head transformer, using physics-inspired geometric and biomechanical priors for self-supervision, and introduces the DynAct4D synthetic benchmark.
LEXIS-Flow uses VQ-VAE-learned interaction signatures to guide diffusion-based reconstruction of 3D human-object meshes and dense proximity fields from single RGB images, outperforming SOTA on benchmarks.
DanceCrafter generates high-fidelity, text-controlled dance sequences using a new Choreographic Syntax framework and a large fine-grained motion dataset.
Vision-language models perform only marginally above random on action quality assessment and retain systematic biases even after targeted prompting and contrastive reformulation.
CoMind releases 41 h of synchronized multi-view cooking collaboration with social-cue annotations and three ToM-oriented benchmarks on which current VLMs score poorly until fine-tuned.
WNM decouples video generation into agent-built editable 4D physical narratives that condition frozen foundation models, reducing gacha-style resampling and improving layout, motion, and camera control.
Introduces a 9136-sample multi-view in-cabin dataset from a German city bus with RGB, depth, LiDAR, 3D annotations via pseudo-labeling, nuScenes conversion, and benchmarks on models like BEVFusion.
ActiveMimic pretrains on egocentric human video by recovering and modeling active camera motion as viewpoint actions, matching robot-data pretraining performance on real-world tasks.
PRIMA boosts 3D quadruped mesh recovery by injecting BioCLIP biological priors and using test-time adaptation with 2D constraints to build the Quadruped3D pseudo-3D dataset and reach SOTA on imbalanced animal benchmarks.
MAEM is a training-free framework that combines monocular 3D mesh recovery with a two-stage epipolar matching strategy using disjoint-set-union clustering and per-joint triangulation for multi-view multi-person 3D pose estimation in basketball.
SUGAR turns diverse human videos into deployable humanoid loco-manipulation policies via automated prior extraction, physics refinement, and hierarchical distillation, showing scaling with data volume and zero-shot real-world transfer on six tasks.
Method converts exocentric videos to egocentric format via body-pose extraction and kinematics to improve egocentric world-model prediction and planning.
HA-HOI produces physically plausible 4D HOI animations from monocular videos by anchoring object reconstruction to human motion and refining the result in a physics-based humanoid-object simulator.
SAMe grounds complaints to organs, builds a lightweight patient anatomy model from one body image, and outputs probe initialization poses, outperforming keypoint baselines in real-robot liver and kidney trials.
CoInteract adds a human-aware mixture-of-experts and spatially-structured co-generation to a diffusion transformer to synthesize videos with stable structures and physically plausible human-object contacts.
Pi-HOC predicts dense 3D semantic contacts for all human-object pairs in an image via instance-aware tokens and an InteractionFormer, achieving higher accuracy and 20x throughput than prior methods.
RoSHI is a hybrid wearable that combines sparse IMUs and egocentric SLAM to capture accurate full-body 3D pose and shape data in natural environments for robot learning.
DUET pretrains collaborative policies on human-human VR demonstrations then fine-tunes on minimal robot teleoperation data, achieving equal or better performance than robot-only baselines with 5.4x faster collection across four tasks.
A hierarchical multi-agent framework converts a single sentence into a short drama using debate-based scripting, 3D-grounded first frames for spatial consistency, and multi-stage reviewer loops.
Asset Harvester converts sparse in-the-wild object observations from AV driving logs into complete simulation-ready 3D assets via data curation, geometry-aware preprocessing, and a SparseViewDiT model that couples sparse-view multiview generation with 3D Gaussian lifting.
A minimal Gaussian splatting avatar pipeline using the Momentum Human Rig achieves the highest reported PSNR on PeopleSnapshot and ZJU-MoCap without learned deformations.
LUNA is an LBS-free neural animation model that maps 2D controls to 3D Gaussian deformations via a transformer motion regressor and hybrid supervision for realistic motion and zero-shot generalization.
citing papers explorer
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CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation
CoInteract adds a human-aware mixture-of-experts and spatially-structured co-generation to a diffusion transformer to synthesize videos with stable structures and physically plausible human-object contacts.
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Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation
Asset Harvester converts sparse in-the-wild object observations from AV driving logs into complete simulation-ready 3D assets via data curation, geometry-aware preprocessing, and a SparseViewDiT model that couples sparse-view multiview generation with 3D Gaussian lifting.