arXiv Machine Learning
Jul 7
Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment
arXiv:2607. 01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving.
Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adverse weather conditions for training perception models or to apply weather-removal techniques to recover clean inputs.
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Hugging Face Trending Papers
Jun 24
UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving
Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion to the planning module, conditioning on fixed outputs from separate discriminative perception networks.
arXiv AI
Jun 30
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
arXiv:2606. 29020v1 Announce Type: cross Abstract: Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion.
Hugging Face Trending Papers
Jun 11
DuET: Dual Expert Trajectories for Diffusion Image Editing
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input.
Hugging Face Trending Papers
Jun 8
EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models
3D semantic scene generation is crucial for autonomous driving applications, yet most methods rely on complex 3D-specific architectures such as triplane encoders and adapted diffusion networks, limiting both their simplicity and their editing capabilities. We propose EditSSC, an editing-ready method for 3D semantic scene generation using 2D Bird's Eye View (BEV) representations and off-the-shelf latent diffusion network.
arXiv AI
Jul 23
OSVE: One Step Video Editing with One Step Diffusion Models
arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.
arXiv AI
Jul 14
OmniSCS: Omni Safety-Critical Scenario Synthesis for Autonomous Driving via a Fully Editable Driving World
arXiv:2607. 09764v1 Announce Type: cross Abstract: The synthesis of safety-critical scenarios (SCS) and their evaluation through closed-loop simulations are crucial for developing robust autonomous driving systems.
arXiv AI
Aug 5
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.
Hugging Face Trending Papers
Jun 29
UniGP: Taming Diffusion Transformer for Prior-Preserved Unified Generation and Perception
Recent advances in diffusion models have shown impressive performance in controllable image generation and dense prediction tasks. However, existing approaches typically treat diffusion-based controllable generation and dense prediction as separate tasks, overlooking the potential benefits of jointly modeling the heterogeneous distributions.
Hugging Face Trending Papers
Aug 4
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.
arXiv Machine Learning
23h ago
From Diffusion to Flow: Efficient Motion Generation in MotionGPT3
arXiv:2603. 26747v3 Announce Type: replace-cross Abstract: Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations.
arXiv AI
Aug 12
Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.