Tag
4DAnyone reconstructs 4D humans from monocular video by generating multiview-consistent videos and lifting them into 4D Gaussian Splatting, using reference and target context designs to address scaling bottlenecks.
ReViV is a unified framework for holistic egocentric 4D reconstruction that simultaneously reconstructs viewer (body, hand, gaze) and view (depth, camera trajectory) dynamics from a single monocular RGB video using a Masked Generative Egocentric Transformer, achieving state-of-the-art accuracy and efficiency.
Proposes StudioRecon, a method for 4D human-scene reconstruction from low-overlap camera captures, using separate priors for backgrounds (video diffusion) and humans (SMPL), with a recursive enhancement module. Accepted to SIGGRAPH 2026, outperforming prior methods by +1.5 to +5.0 dB PSNR.
Lift4D is a test-time optimization framework that reconstructs complete 4D geometry, appearance, and deformation of dynamic objects from a single monocular in-the-wild video, improving over prior methods on challenging sequences with occlusions and non-rigid motion.
Multi4D introduces a multi-level competitive allocation framework for dynamic 3D Gaussian splatting that balances motion consistency and visual fidelity, achieving state-of-the-art rendering quality and real-time performance.
DeepMind introduces D4RT, a unified AI model for dynamic 4D scene reconstruction and tracking that is up to 300x more efficient than previous methods. The model uses a query-based Transformer architecture to solve complex spatial and temporal tasks for robotics and AR applications.