first-frame-video-customization

name	first-frame-video-customization
title	First Frame Is the Place to Go for Video Content Customization
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2511.15700
keywords	["Video Generation","Content Customization","First Frame","Few-Shot Adaptation","Minimal Retraining"]
description	Enable video generation customization via first-frame reuse—treat initial frame as a visual entity buffer storing objects for reuse during generation with just 20-50 examples, requiring minimal architectural change.

Customize Video Generation by Leveraging the First Frame as Visual Buffer

Video generation models treat the first frame as a conditioning seed—it establishes spatial layout and visual context. This paper reveals the first frame plays a deeper role: it functions as a visual entity buffer that video models reference and reuse throughout generation. By explicitly leveraging this mechanism with minimal training data (20-50 examples), users can customize video content—changing object appearances, poses, and interactions—with no architectural modifications.

The insight is that existing diffusion video models already have this capability embedded; we just need to exploit it with light few-shot fine-tuning.

Core Concept

Video diffusion models condition on a first frame, then iteratively generate subsequent frames. Conventional wisdom treats the first frame as:

• Spatial blueprint: Establishes which regions contain objects
• Style guide: Provides color/texture palette

This paper demonstrates the first frame is actually more: a semantic entity repository. The model internally references the first frame's visual entities (objects, characters, textures) throughout the generation process, reusing them as building blocks for coherent video generation.

Exploiting this for customization is straightforward: provide a custom first frame with desired entity variations, and the model naturally propagates those variations through the generated video with minimal retraining.

Architecture Overview

• First Frame Conditioning: Encode first frame as visual entities rather than raw pixels
• Entity Extraction: Identify and encode key visual entities from first frame (objects, characters, backgrounds)
• Reuse Mechanism: Diffusion model's attention layers naturally attend to and reuse first-frame entities
• Few-Shot Adaptation: Fine-tune entity embeddings on small dataset (20-50 examples) for new entity variations
• No Architectural Change: Leverage existing video model; only adapt input encoding and embedding layers

Implementation Steps

Step 1: Extract Visual Entities from First Frame.

import torch
import torch.nn as nn

class FirstFrameEntityExtractor(nn.Module):
    """
    Extract and encode visual entities from first frame.
    Entities: distinct objects, characters, textures, etc.
    """
     ():
        ().__init__()

        
        .vision_encoder = load_vision_model(vision_model_name)
        .embedding_dim = embedding_dim

        
        .segmenter = load_segmentation_model()  

        
        .entity_encoder = nn.Sequential(
            nn.Linear(, ),  
            nn.ReLU(),
            nn.Linear(, embedding_dim)
        )

     ():
        
        
        frame_features = .vision_encoder(first_frame.unsqueeze())  

        
        entity_masks = .segmenter(first_frame)  

        
        entity_embeddings = []
        entity_locations = []

         mask  entity_masks:
            
            entity_region = first_frame * mask.unsqueeze()

            
            entity_feat = .vision_encoder(entity_region.unsqueeze())
            entity_emb = .entity_encoder(entity_feat)
            entity_embeddings.append(entity_emb)

            
            entity_loc = torch.where(mask > )
            centroid = (entity_loc[].().mean(), entity_loc[].().mean())
            entity_locations.append(centroid)

        entity_embeddings = torch.cat(entity_embeddings, dim=)  

         {
            : entity_embeddings,
            : entity_masks,
            : entity_locations,
            : frame_features
        }