Research Papers

CAP4D introduces a unified framework for generating photorealistic and animate style rendering 4D portrait avatars from any number of reference images as well as even a single image. By combining

Test3R is a novel and simple test-time learning technique that significantly improves 3D reconstruction quality. Unlike traditional pairwise methods such as DUSt3R, which often suffer from geometric inconsistencies and poor generalization,

BlenderFusion is a novel framework that merges 3D graphics editing with diffusion models to enable precise, 3D-aware visual compositing. Unlike prior approaches that struggle with multi-object and camera disentanglement, BlenderFusion

LongSplat is a new framework that achieves high-quality novel view synthesis from casually captured long videos, without requiring camera poses. It overcomes challenges like irregular motion, pose drift, and memory

DINOv3 is a next-generation vision foundation model trained purely with self-supervised learning. It introduces innovations that allow robust dense feature learning at scale with models reaching 7B parameters and achieves

Genie 3 is a general-purpose world model which, given just a text prompt, generates dynamic, interactive environments in real time and rendered at 720p, 24 fps, while maintaining consistency over

PartCrafter is the first unified 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D parts from a single RGB image without any segmentation required. Powered by

SimLingo unifies autonomous driving, vision-language understanding, and action reasoning-all from camera input only. It introduces Action Dreaming to test how well models follow instructions, and outperforms all prior methods on

SAM4D introduces a 4D foundation model for promptable segmentation across camera and LiDAR streams, addressing the limitations of frame-centric and modality-isolated approaches in autonomous driving. Key Highlights: Paper Resources

VideoGameBench is a rigorous benchmark that evaluates VLMs’ real-time decision-making, perception, memory, and planning by challenging them to complete 1990s-era video games with only raw visual inputs and minimal control

LeGO-LOAM introduces a cutting-edge lidar odometry and mapping framework designed to deliver real-time, accurate 6-DOF pose estimation for ground vehicles, optimized for challenging, variable terrain environments. It significantly reduces computational

Reliable-loc introduces a resilient LiDAR-based global localization system for wearable mapping devices in complex, GNSS-denied street environments with sparse features and incomplete prior maps. Key Highlights: Paper Resources Related articles