SceneWeaver: A Drift-Aware Multimodal Framework for Long Video Generation

SceneWeaver is an innovative framework that addresses temporal fragmentation and narrative inconsistency in diffusion model-based video generation. Its core lies in the introduction of a drift-aware mechanism, enabling high-quality long text-to-video generation and providing a key solution for the long video generation field.

Basic Principles of Diffusion Models

Diffusion models generate high-quality images through forward diffusion (adding noise) and reverse denoising (recovering images). However, when extended to video generation, they face unique challenges:

1. Temporal Consistency: Need to maintain coherence in appearance, motion, and scenes across frames;
2. Long-Range Dependencies: Need to preserve consistency in characters, plots, and themes;
3. Computational Complexity: High-dimensional video data leads to issues with memory, training, and inference costs. Existing solutions (frame-by-frame generation, sliding window, hierarchical generation) generally suffer from the "drift" problem, resulting in incoherent narratives in long videos.

Core Idea

Introduce a drift-aware mechanism: Monitor the consistency between generated content and text, correct deviations, and maintain narrative and visual coherence.

Architecture Design

1. Text Understanding and Scene Planning: Semantic parsing, scene decomposition, key information extraction;
2. Drift Detection Module: Content/temporal/long-range consistency evaluation;
3. Adaptive Generation Strategy: Dynamic parameter adjustment, key frame anchoring, attention guidance;
4. Post-Processing Optimization: Temporal smoothing, style unification, quality enhancement.

Long-Range Dependency Modeling

• Hierarchical attention (local + global + cross-layer interaction);
• Memory-enhanced network (external memory storage, selective reading, dynamic update).

Narrative Coherence Preservation

• Plot graph modeling (event extraction, causal relationship, plot progression);
• Character consistency mechanism (feature encoding, cross-frame tracking, feature consistency).

Computational Efficiency Optimization

• Block-based generation (intelligent blocking, overlapping regions, parallel processing);
• Cascaded generation (coarse-to-fine, key frame priority, adaptive refinement).

1. Film and Television Production: Pre-visualization, concept videos, special effects preview, animation assistance;
2. Advertising Creativity: Creative iteration, personalized content, multilingual versions;
3. Education and Training: Teaching videos, scenario simulation, language learning;
4. Game Development: Cutscenes, NPC behavior, scene generation.

Evaluation Metrics

• Generation Quality: FVD, IS, CLIP Score;
• Consistency Metrics: Character/style/narrative coherence scores;
• Human Evaluation: Overall quality, consistency, text alignment scores.

Comparative Advantages

Maintains better quality in long video generation, significantly improves temporal and character consistency, and has narrative logic that better meets requirements.

Current Limitations

High computational cost, slow generation speed, limited handling of complex scenes, insufficient adherence to physical laws.

Future Directions

Real-time generation, interactive generation, multi-modal input support, fine-grained controllable generation (camera movement, character actions, etc.).