Memento Framework: Subject-Reconstruction Guided Long Video Generation for Consistency

arXiv CS · · 2 min read · Engineering & Technology

Read research and analysis on Memento Framework: Subject-Reconstruction Guided Long Video Generation for Consistency published by ICANEWS, a global research journal for emerging researchers.

Key Takeaways

  • Memento jointly trains autoregressive next-shot generation with memory-based subject reconstruction.
  • The framework introduces a dual-query memory mechanism to disentangle long-range subject evidence from short-range cues.
  • A subject-aware cinematic data pipeline provides precise reconstruction supervision through consistent, pronoun-free subject descriptions.
  • Experiments showed Memento achieves state-of-the-art performance in long-term subject consistency, cross-shot coherence, and visual quality.

Why This Matters

The framework addresses a core challenge in long-form video generation: maintaining consistent subject identity over extended durations and various scene changes. By ensuring recurring subjects are not diluted or forgotten, it contributes to the realism and narrative integrity of generated long videos.

Overview

The Memento framework is proposed to address challenges in long-form video generation, particularly concerning the consistent representation of recurring subjects across varying shots, viewpoints, motions, and scene transitions. It approaches subject preservation as an identity grounding problem, founded on the principle that a reliable memory bank for a subject should facilitate its reconstruction from memory.

Research Context

Long-form video generation necessitates that recurring subjects maintain consistency throughout the video's duration. Existing temporal decomposition methods contribute to scalability by generating videos on a shot-by-shot basis. However, these methods primarily focus on optimizing plausible next-shot continuations. A recognized limitation identified is their lack of verification regarding whether historical memory adequately preserves identity-critical subject evidence. This can lead to recurring subjects becoming diluted, overwritten, or forgotten as the generation process continues.

Approach

Memento operates on the premise that if a memory bank faithfully preserves a subject, it should enable the reconstruction of that subject using only the stored memory. The framework jointly trains two components: autoregressive next-shot generation and memory-based subject reconstruction. The reconstruction component aims to recover target appearances by utilizing historical memory in conjunction with global story captions.

To distinguish between long-range subject evidence and short-range contextual cues, Memento incorporates a dual-query memory mechanism. Within this mechanism, one query is designed to retrieve memory relevant to the subject's identity, while the second query selects short-context keyframes to support coherent continuation of the video sequence.

Furthermore, Memento integrates a subject-aware cinematic data pipeline. This pipeline's function is to provide precise reconstruction supervision. It achieves this through the use of consistent, pronoun-free subject descriptions, which aid in maintaining the integrity of subject representation during the generation process.

Findings

Experiments conducted on the Memento framework indicated that it achieved state-of-the-art performance. This performance was observed across several evaluation criteria: long-term subject consistency, cross-shot coherence, and overall visual quality.

Research Information

Institution
arXiv
Original Study
View Publication
Source
arXiv CS

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