REVIEW 10 cited by
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-scene optimization. A key challenge lies in finding an efficient and generalizable geometric representation that seamlessly connects temporal and spatial synthesis. To address this, we propose DiST-4D, the first disentangled spatiotemporal diffusion framework for 4D driving scene generation, which leverages metric depth as the core geometric representation. DiST-4D decomposes the problem into two diffusion processes: DiST-T, which predicts future metric depth and multi-view RGB sequences directly from past observations, and DiST-S, which enables spatial NVS by training only on existing viewpoints while enforcing cycle consistency. This cycle consistency mechanism introduces a forward-backward rendering constraint, reducing the generalization gap between observed and unseen viewpoints. Metric depth is essential for both accurate reliable forecasting and accurate spatial NVS, as it provides a view-consistent geometric representation that generalizes well to unseen perspectives. Experiments demonstrate that DiST-4D achieves state-of-the-art performance in both temporal prediction and NVS tasks, while also delivering competitive performance in planning-related evaluations.
Forward citations
Cited by 10 Pith papers
-
Point as Skeleton: Accumulated Point Cloud Enhanced Autoregressive Generation for Closed-Loop Autonomous Driving Simulation
Point-cloud skeleton conditions and a Reset-and-Roll inference scheme enable stable frame-wise autoregressive driving video generation for closed-loop autonomous driving simulation.
-
DVGT: Driving Visual Geometry Transformer
DVGT predicts metric-scaled global 3D point maps and ego poses from unposed multi-view driving video, beating prior geometry models on several driving benchmarks.
-
GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation
GeoWorld improves image-to-3D scene generation by conditioning a video-diffusion model on full-frame geometry features extracted by a multi-view geometry model, yielding higher PSNR/SSIM/LPIPS than prior methods.
-
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
A reward-only offline RL method for trajectory planning in end-to-end autonomous driving achieves state-of-the-art on Navhard and competitive closed-loop HUGSIM performance without imitation learning.
-
OmniNWM: Omniscient Driving Navigation World Models
OmniNWM jointly generates long panoramic multi-modal driving videos, controls them precisely via normalized Plücker ray-maps, and derives dense driving rewards from generated 3D occupancy.
-
GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting
A camera-only Gaussian-surfel pipeline reconstructs full Waymo scenes, converts them to binary occupancy labels, and trains CVT-Occ to generalize on Occ3D-Waymo and Occ3D-nuScenes at a level close to or above LiDAR-la...
-
ArbiViewGen: Controllable Arbitrary Viewpoint Camera Data Generation for Autonomous Driving via Stable Diffusion Models
ArbiViewGen generates arbitrary-viewpoint driving camera images by stitching the six input views into pseudo-target views and training a Stable Diffusion model to reconstruct the original views, enabling self-supervis...
-
CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting
CRUISE reconstructs real V2X driving scenes as editable Gaussians, then shows that training on its generated data improves 3D detection and tracking on the V2X-Seq benchmark.
-
LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model
A hierarchical coarse-to-fine diffusion transformer with cross-granularity distillation improves long-term driving video prediction, but the reported gains may be inflated by future-derived text prompts and a selected...
-
Challenger: Affordable Adversarial Driving Video Generation
A framework for automatic generation of photorealistic adversarial driving videos, shown to sharply increase collision rates of end-to-end autonomous driving models.
Discussion (0). Continue with ORCID to comment.