ARCON: Advancing Auto-Regressive Continuation for Driving Videos

• URL: http://arxiv.org/abs/2412.03758v3
• Date: Wed, 26 Feb 2025 18:16:15 GMT
• Title: ARCON: Advancing Auto-Regressive Continuation for Driving Videos
• Authors: Ruibo Ming, Jingwei Wu, Zhewei Huang, Zhuoxuan Ju, Jianming HU, Lihui Peng, Shuchang Zhou,
• Abstract summary: This paper explores the use of Large Vision Models (LVMs) for video continuation.<n>We introduce ARCON, a scheme that alternates between generating semantic and RGB tokens, allowing the LVM to explicitly learn high-level structural video information.<n> Experiments in autonomous driving scenarios show that our model can consistently generate long videos.
• Score: 7.958859992610155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for video continuation, a task essential for building world models and predicting future frames. We introduce ARCON, a scheme that alternates between generating semantic and RGB tokens, allowing the LVM to explicitly learn high-level structural video information. We find high consistency in the RGB images and semantic maps generated without special design. Moreover, we employ an optical flow-based texture stitching method to enhance visual quality. Experiments in autonomous driving scenarios show that our model can consistently generate long videos.

Related papers

• SkyReels-V2: Infinite-length Film Generative Model [35.00453687783287]
  We propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. We establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement.
  arXiv  Detail & Related papers  (2025-04-17T16:37:27Z)
• VaViM and VaVAM: Autonomous Driving through Video Generative Modeling [88.33638585518226]
  We introduce an open-source auto-regressive video model (VaM) and its companion video-action model (VaVAM) to investigate how video pre-training transfers to real-world driving. We evaluate our models in open- and closed-loop driving scenarios, revealing that video-based pre-training holds promise for autonomous driving.
  arXiv  Detail & Related papers  (2025-02-21T18:56:02Z)
• VideoAuteur: Towards Long Narrative Video Generation [22.915448471769384]
  We present a large-scale cooking video dataset designed to advance long-form narrative generation in the cooking domain. We introduce a Long Narrative Video Director to enhance both visual and semantic coherence in generated videos. Our method demonstrates substantial improvements in generating visually detailed and semantically aligneds.
  arXiv  Detail & Related papers  (2025-01-10T18:52:11Z)
• DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT [33.943125216555316]
  We present DrivingWorld, a GPT-style world model for autonomous driving. We propose a next-state prediction strategy to model temporal coherence between consecutive frames. We also propose a novel masking strategy and reweighting strategy for token prediction to mitigate long-term drifting issues.
  arXiv  Detail & Related papers  (2024-12-27T07:44:07Z)
• DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving [12.004604110512421]
  Vision language models (VLMs) are emerging as revolutionary tools with significant potential to influence autonomous driving. We propose the DriveGenVLM framework to generate driving videos and use VLMs to understand them.
  arXiv  Detail & Related papers  (2024-08-29T15:52:56Z)
• iVideoGPT: Interactive VideoGPTs are Scalable World Models [70.02290687442624]
  World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. This work introduces Interactive VideoGPT, a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations.
  arXiv  Detail & Related papers  (2024-05-24T05:29:12Z)
• Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
  Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest. This technique allows LVLMs to access more detailed visual information without altering the original image resolution. Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
  arXiv  Detail & Related papers  (2024-03-19T17:59:52Z)
• Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback [38.708690624594794]
  Video and text multimodal alignment remains challenging, primarily due to the deficient volume and quality of multimodal instruction-tune data. We present a novel alignment strategy that employs multimodal AI system to oversee itself called Reinforcement Learning from AI Feedback (RLAIF) In specific, we propose context-aware reward modeling by providing detailed video descriptions as context during the generation of preference feedback.
  arXiv  Detail & Related papers  (2024-02-06T06:27:40Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets [36.95521842177614]
  We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. We identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning.
  arXiv  Detail & Related papers  (2023-11-25T22:28:38Z)
• Conditional Generative Modeling for Images, 3D Animations, and Video [4.422441608136163]
  dissertation attempts to drive innovation in the field of generative modeling for computer vision. Research focuses on architectures that offer transformations of noise and visual data, and the application of encoder-decoder architectures for generative tasks and 3D content manipulation.
  arXiv  Detail & Related papers  (2023-10-19T21:10:39Z)
• VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation [73.54366331493007]
  VideoGen is a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt.
  arXiv  Detail & Related papers  (2023-09-01T11:14:43Z)
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
  Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
  arXiv  Detail & Related papers  (2023-05-23T09:03:19Z)
• Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models [149.1331903899298]
  We propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge. We present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model.
  arXiv  Detail & Related papers  (2022-12-31T11:36:53Z)
• Masked World Models for Visual Control [90.13638482124567]
  We introduce a visual model-based RL framework that decouples visual representation learning and dynamics learning. We demonstrate that our approach achieves state-of-the-art performance on a variety of visual robotic tasks.
  arXiv  Detail & Related papers  (2022-06-28T18:42:27Z)
• TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator [34.7504057664375]
  We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. Step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions.
  arXiv  Detail & Related papers  (2020-09-04T06:33:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.