Kerui Ren

Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.

Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.

GeoVerse performs diffusion generation inside the geometric latent space of a pretrained 3D foundation model (DA3), injects frozen Wan2.2 VACE video features into the denoiser through a ControlNet-style adapter, and uses a continuously updated colored point-cloud spatial memory to reproject history as target-aligned guidance, synthesizing cross-view consistent novel views from sparse inputs; across DL3DV, RealEstate10K, Mip-NeRF 360, and ScanNetV2 it reports better visual quality and geometric consistency, with PSNR 2.23 dB higher than GLD on DL3DV and ATE 32.4% lower than GLD on Mip-NeRF 360, while reflow distillation brings inference from over 100 seconds to under 10 seconds.

GeoVerse performs diffusion generation inside the geometric latent space of a pretrained 3D foundation model (DA3), injects frozen Wan2.2 VACE video features into the denoiser through a ControlNet-style adapter, and uses a continuously updated colored point-cloud spatial memory to reproject history as target-aligned guidance, synthesizing cross-view consistent novel views from sparse inputs; across DL3DV, RealEstate10K, Mip-NeRF 360, and ScanNetV2 it reports better visual quality and geometric consistency, with PSNR 2.23 dB higher than GLD on DL3DV and ATE 32.4% lower than GLD on Mip-NeRF 360, while reflow distillation brings inference from over 100 seconds to under 10 seconds.

InfiniHand is an end-to-end streaming feed-forward framework that jointly estimates MANO parameters, camera trajectories, and hand locations directly from uncalibrated egocentric video, trained in two progressive stages on roughly 5,000 hours of aggregated egocentric data; it reduces ARCTIC PA-p by 21.4% versus ViDiHand (9.82 to 7.72 mm), lowers EgoDex W-MPJPE from 78.41 to 28.77 mm versus Dyn-HaMR, and runs at 11.19 FPS, more than twice the throughput of HaWoR.

InfiniHand is an end-to-end streaming feed-forward framework that jointly estimates MANO parameters, camera trajectories, and hand locations directly from uncalibrated egocentric video, trained in two progressive stages on roughly 5,000 hours of aggregated egocentric data; it reduces ARCTIC PA-p by 21.4% versus ViDiHand (9.82 to 7.72 mm), lowers EgoDex W-MPJPE from 78.41 to 28.77 mm versus Dyn-HaMR, and runs at 11.19 FPS, more than twice the throughput of HaWoR.