GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling

• URL: http://arxiv.org/abs/2606.20799v1
• Date: Thu, 18 Jun 2026 18:00:03 GMT
• Title: GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling
• Abstract summary: We present textbfGroundShot, a training-free, model-agnostic agentic framework for entity-grounded multi-shot generation.<n>GroundShot builds an entity-level visual memory online from accepted generated shots.<n>Experiments show that GroundShot improves multi-shot consistency over existing methods while requiring no additional training or model modification.
• Score: 48.381235555561965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating visually consistent multi-shot videos remains an open challenge. As videos span more shots, inconsistencies can accumulate across shots, causing entities that reappear across shots -- characters, objects, and locations -- to drift away from how they first appear. We observe that viewers judge consistency by comparing each later appearance of an entity with its first clear appearance; the visual quality of this initial appearance sets the consistency ceiling for all that follows. Motivated by this, we present \textbf{GroundShot}, a training-free, model-agnostic agentic framework for entity-grounded multi-shot generation. GroundShot builds an entity-level visual memory online from accepted generated shots: it schedules shots' generation order by their expected usefulness as entity references, grounds entities from generated videos, verifies their reliability before adding them to memory, and retrieves suitable entity references from memory before each shot is generated. To evaluate this entity-centered view of consistency, we further introduce \textbf{GroundBench}, a diagnostic benchmark that measures consistency at the entity level while isolating controlled challenge dimensions. Experiments show that GroundShot improves multi-shot consistency over existing methods while requiring no additional training or model modification.

Related papers

• MSEditor: Toward Consistent Multi-Shot Video Editing [46.964066826513374]
  We propose MSEditor, the first framework designed specifically for consistent multi-shot video editing.<n>To overcome the scarcity of high-quality multi-shot training data, we repurpose existing multi-view video datasets to provide robust cross-shot supervision.<n>To effectively mitigate cumulative errors and ensure long-range temporal coherence, we design a Cross-Shot Packing strategy.
  arXiv  Detail & Related papers  (2026-08-18T09:21:58Z)
• Closed-Loop Triplet Synergistic Generation for Long-Form Video [61.88597038104749]
  CoSyTriGen is an agentic framework that formulates multi-shot long video generation as a closed-loop visual-text-memory synergy process.<n>A vision-language-model-based analyzer reasons over this triplet and produces updates to both prompts and memory along two pathways.<n> Experiments on our StoryBench benchmark demonstrate substantial improvements in cross-shot consistency, prompt adherence, and cinematic continuity over representative methods.
  arXiv  Detail & Related papers  (2026-06-15T03:56:43Z)
• EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation [16.322441569982647]
  We introduce EntityBench, a benchmark of 140 episodes (2,491 shots) derived from real narrative media.<n>It is paired with a three-pillar evaluation suite that disentangles intra-shot quality, prompt-following alignment, and cross-shot consistency.<n> Experiments show that cross-shot entity consistency degrades sharply with recurrence distance in existing methods.
  arXiv  Detail & Related papers  (2026-05-14T17:59:55Z)
• PLACID: Identity-Preserving Multi-Object Compositing via Video Diffusion with Synthetic Trajectories [22.63777279327245]
  PLACID is a framework that transforms a collection of object images into an appealing multi-object composite.<n>First, we leverage a pretrained image-to-video (I2V) diffusion model with text control to preserve objects consistency, identities, and background details.<n>Second, we propose a novel data curation strategy that generates synthetic sequences where randomly placed objects smoothly move to their target positions.
  arXiv  Detail & Related papers  (2026-01-30T19:42:54Z)
• VideoMemory: Toward Consistent Video Generation via Memory Integration [28.605816634949814]
  VideoMemory integrates narrative planning with visual generation through a Dynamic Memory Bank.<n>The Dynamic Memory Bank stores explicit visual and semantic descriptors for characters, props, and backgrounds.<n>This retrieval-update mechanism enables consistent portrayal of entities across distant shots and supports coherent long-form generation.
  arXiv  Detail & Related papers  (2026-01-07T07:10:32Z)
• FilmWeaver: Weaving Consistent Multi-Shot Videos with Cache-Guided Autoregressive Diffusion [46.67733869872552]
  textbfFilmWeaver is a framework designed to generate consistent, multi-shot videos of arbitrary length.<n>Our key insight is to decouple the problem into inter-shot consistency and intra-shot coherence.<n>Our method surpasses existing approaches on metrics for both consistency and aesthetic quality.
  arXiv  Detail & Related papers  (2025-12-12T04:34:53Z)
• AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation [58.844504598618094]
  We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation.<n>Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities.<n>We incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation.
  arXiv  Detail & Related papers  (2025-12-11T18:59:34Z)
• The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment [105.31858867473845]
  ImageCritic can be integrated into an agent framework to automatically detect inconsistencies and correct them with multi-round and local editing.<n>In experiments, ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.
  arXiv  Detail & Related papers  (2025-11-25T18:40:25Z)
• Consistent Human Image and Video Generation with Spatially Conditioned Diffusion [82.4097906779699]
  Consistent human-centric image and video synthesis aims to generate images with new poses while preserving appearance consistency with a given reference image.<n>We frame the task as a spatially-conditioned inpainting problem, where the target image is in-painted to maintain appearance consistency with the reference.<n>This approach enables the reference features to guide the generation of pose-compliant targets within a unified denoising network.
  arXiv  Detail & Related papers  (2024-12-19T05:02:30Z)
• Video Demoireing with Relation-Based Temporal Consistency [68.20281109859998]
  Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. We study how to remove such undesirable moire patterns in videos, namely video demoireing.
  arXiv  Detail & Related papers  (2022-04-06T17:45:38Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

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.