Search Results for author:
Found 8 papers, 1 papers with code
Physical Informed Driving World Model
Autonomous driving requires robust perception models trained on high-quality, large-scale multi-view driving videos for tasks like 3D object detection, segmentation and trajectory prediction.
InfinityDrive: Breaking Time Limits in Driving World Models
Comprehensive experiments in multiple datasets validate InfinityDrive's ability to generate complex and varied scenarios, highlighting its potential as a next-generation driving world model built for the evolving demands of autonomous driving.
MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control
High-quality driving video generation is crucial for providing training data for autonomous driving models.
SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook
Utilizing inference results from segmentation model , our approach constructs a temporospatially consistent semantic codebook, addressing issues of codebook collapse and imbalanced token semantics.
DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generation
However, existing approaches often struggle with multi-view video generation due to the challenges of integrating 3D information while maintaining spatial-temporal consistency and effectively learning from a unified model.
PhysReaction: Physically Plausible Real-Time Humanoid Reaction Synthesis via Forward Dynamics Guided 4D Imitation
Humanoid Reaction Synthesis is pivotal for creating highly interactive and empathetic robots that can seamlessly integrate into human environments, enhancing the way we live, work, and communicate.
StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views
We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf, that is readily applicable to street view images in widely-used autonomous driving datasets, such as Waymo-perception sequences, without necessarily requiring LiDAR data.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation
We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions.
