Pith. sign in

REVIEW 4 cited by

Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2311.16813 v1 pith:KW32EWAR submitted 2023-11-28 cs.CV

Yuqing Wen , Yucheng Zhao , Yingfei Liu , Fan Jia , Yanhui Wang , Chong Luo , Chi Zhang , Tiancai Wang
show 2 more authors
Xiaoyan Sun Xiangyu Zhang
This is my paper
classification cs.CV
keywords drivingautonomouspanaceaannotatedapproachcoherenceconsistencycontrollability
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and controllable videos in driving scenarios, capable of yielding an unlimited numbers of diverse, annotated samples pivotal for autonomous driving advancements. Panacea addresses two critical challenges: 'Consistency' and 'Controllability.' Consistency ensures temporal and cross-view coherence, while Controllability ensures the alignment of generated content with corresponding annotations. Our approach integrates a novel 4D attention and a two-stage generation pipeline to maintain coherence, supplemented by the ControlNet framework for meticulous control by the Bird's-Eye-View (BEV) layouts. Extensive qualitative and quantitative evaluations of Panacea on the nuScenes dataset prove its effectiveness in generating high-quality multi-view driving-scene videos. This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    A feed-forward model reconstructs a layered, simulation-ready 3D Gaussian world from multi-view driving video in ~1.5 s, with quality approaching per-scene optimized reconstruction.

  2. Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A single reference image guides a diffusion model to insert coherent objects into camera-plus-lidar driving scenes and to insert mammographic anomalies into new scans.

  3. ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ProphetDWM is a one-stage diffusion world model that jointly predicts future driving video and low-level actions from a current frame and a short action sequence.

  4. Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Diffusion-based generative models, using discrete categorical diffusion conditioned on BEV features, improve 3D occupancy prediction and downstream planning for autonomous driving.

Pith tools