Source-linked AI summary
Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts
Yuyang Zhao, Enze Xie, Lanqing Hong, Zhenguo Li, Gim Hee Lee
TL;DR
Text-only editing and single-reference personalization have limits when target content is difficult to describe or requires fine-grained control. Make-A-Protagonist combines visual and textual clues with source-video information through a video generation model and mask-guided fusion. The paper reports versatile editing applications and user-study preference over baselines on video quality, subject fidelity, and prompt fidelity.
Problem
Text-based editing cannot reliably specify indescribable content, while personalization and variation methods are limited by fine-tuning needs and reliance on a single visual clue.
Source: §1 · p. 2Method
Make-A-Protagonist combines source-video information and visual and textual clues in a video generation model using mask-guided fusion.
Source: §1 · p. 2, §3.2 · p. 4Results
The user study reports overwhelming preference for Make-A-Protagonist over the compared methods in video quality, subject fidelity, and prompt fidelity.
Source: §4.2 · p. 8Takeaways & Limitations
The framework supports protagonist editing, background editing, and text-to-video editing with the protagonist.
Source: §5 · p. 8The authors note that personalized image generation raises misuse risk and recommend safety checks for source videos and reference images.
Source: §5 · p. 9
Abstract
from arXiv · showhide
The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have garnered significant attention. However, previous works are limited to editing content with text or providing coarse personalization using a single visual clue, rendering them unsuitable for indescribable content that requires fine-grained and detailed control. In this regard, we propose a generic video editing framework called Make-A-Protagonist, which utilizes textual and visual clues to edit videos with the goal of empowering individuals to become the protagonists. Specifically, we leverage multiple experts to parse source video, target visual and textual clues, and propose a visual-textual-based video generation model that employs mask-guided denoising sampling to generate the desired output. Extensive results demonstrate the versatile and remarkable editing capabilities of Make-A-Protagonist.
1. Introduction
Text-only editing cannot reliably handle content that is difficult to describe, while personalization and variation methods have limits in reference-image count and training requirements. Make-A-Protagonist combines visual and textual clues for end-to-end generic video editing.
1. Introduction
- Text-based methods can replace recognizable cars but cannot reliably substitute an unnamed car that lacks a precise language-model-recognized name [19] [25].The paper uses an unnamed black car as an example of content that cannot be accurately conveyed through text.Source: §1 · p. 2
- Personalized models require target-specific fine-tuning with multiple images, while variation models tend toward reference backgrounds and typically use one visual clue.These limitations motivate editing with text and an arbitrary number of reference images.Source: §1 · p. 2
- Tune-A-Video combined with personalized modeling requires retraining two models when the reference image changes and remains sensitive to hyperparameters [38].The paper responds by disentangling personally edited protagonists from the source video for end-to-end, one-stage editing.Source: §1 · p. 2
- Make-A-Protagonist uses a text-to-video model and mask-guided fusion to combine visual and textual clues with source motion, using masks from pretrained models without data annotation.The stated editing applications include protagonist editing while preserving the background, background editing, and text-to-video editing with the protagonist.Source: §1 · p. 2
2. Related Work
Prior work spans text-conditioned image and video generation, text-based editing, and visual variation or personalization. The paper presents generic video editing with both image and text descriptions as its response to limitations in these approaches.
2. Related Work
- Diffusion models advanced text-to-image generation, while text-to-video work uses pretrained image models and temporal modules, with some methods also establishing temporal consistency in latent space [15] [20].The passage cites text-to-image methods [27] [28] [31] and text-to-video methods [11] [12] [20] [34] [41].Source: §2 · pp. 2–3
- Text-based image and video editing methods modify images, attention maps, video layers, or key frames, but remain unsuitable for content that is difficult to describe.Examples include SDEdit [21], Prompt-to-prompt [8], Tune-A-Video [38], and TokenFlow [7].Source: §2 · p. 3
- CLIP-based variation can incorporate background information, while personalized models may require multiple images and configuration-sensitive fine-tuning; Make-A-Protagonist proposes using both image and text descriptions.The framework’s inference overview shows source-video masks and control signals used with visual and textual clues in mask-guided fusion.Source: §2 · p. 3, Figure 2 · p. 3
3. Make-A-Protagonist
Make-A-Protagonist combines visual and textual clues for generic video editing, using source-video masks and attention to control where and how each clue is applied. Its applications edit the protagonist, the background, or both.
3.1. Overview
- The framework combines visual and textual clues with source-video motion and structure, using mask-guided fusion to control spatial placement during inference.DDIM inversion supplies the starting latent code to preserve source-video motion and structure and enhance temporal consistency.Source: §3.1 · p. 3, §3.2 · p. 4
3.2. Visual-Textual-based Video Generation Model
- The video generation model conditions on video captions and randomly sampled reference frames, using temporal modules to capture correlations across frames.BLIP-2 extracts captions for CLIP text embeddings used in cross-attention; CLIP image embeddings from a sampled frame are added to U-Net residual blocks.Source: §3.2 · p. 4, Figure 3 · p. 4
3.3. Mask-Guided Fusion
- Mask-guided fusion addresses coarse spatial control by combining reference-image and text-derived embeddings, source-video attention, and ControlNet features for protagonist control.The DALL-E 2 Prior converts text into an image embedding, while source-video self-attention helps guide background information; ControlNet uses pose or depth conditions within source masks.Source: §3.3 · p. 5
3.4. Applications of Make-A-Protagonist
- The model supports protagonist editing, background editing, and simultaneous editing of both, assigning image and text embeddings to the desired content in each application.For protagonist editing, source-video frames provide the background embedding; for background editing, they provide the protagonist embedding, while text describes the new background.Source: §3.4 · p. 5
4. Experiments
The experiments compare Make-A-Protagonist with generic video-editing baselines and evaluate its components through ablations. The reported results favor it in user preferences and show distinct effects from removing proposed components.
4.1. Implementation Details
- The video model is fine-tuned on eight 768 × 768 frames for 200 steps; training takes about 10 minutes per video and inference about 30 seconds.Inference uses 20 DDIM steps with classifier-free guidance.Source: §4.1 · p. 6
4.2. Comparison with Baselines
- Make-A-Protagonist has comparable model evaluation to DreamBooth-V and overwhelming user-study preference for video quality, subject fidelity, and prompt fidelity.The study asked 20 users 25 comparative questions; DreamBooth-V obtained a higher DINO score, which the authors conjecture may reflect overfitting to the reference image.Source: Table 1 · p. 7, §4.2 · pp. 7–8
- Text-based baselines cannot use visual clues, Gen-1 cannot combine image and text, and DreamBooth-V struggles with complex subjects and editing multiple protagonists.DreamBooth-V also requires a separate model per protagonist and careful training-configuration selection.Source: §4.2 · p. 7
4.3. Ablation Studies
- Ablations link ControlNet to motion and spatial accuracy, DALL-E 2 Prior to text alignment, feature fusion to visual and prompt fidelity, and attention fusion to temporal consistency.Removing feature fusion reduces DINO by 0.013; attention fusion addresses inconsistent lane markings.Source: §4.3 · p. 8
5. Conclusion
Make-A-Protagonist combines textual and visual clues with mask-guided fusion for generic video editing. Its visual representation may vary in effectiveness across subjects, and the authors note misuse risks from personalized image generation.
5. Conclusion
- Make-A-Protagonist combines visual-textual video generation with mask-guided fusion to edit protagonists, backgrounds, or both.The framework uses source-video masks, control signals, and attention maps to provide spatial locations for editing.Source: §5 · p. 8
- CLIP image embeddings may not capture all subject variations, and visual-representation effectiveness varies by subject, with better performance on cars than humans.Source: §5 · p. 8
- Personalized image generation heightens misuse risk, motivating safety checks for source videos and reference images.Source: §5 · p. 9