InstructVid2Vid: Controllable Video Editing with Natural Language Instructions (2305.12328v2)

Abstract: We introduce InstructVid2Vid, an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by natural language directives, eliminating the need for per-example fine-tuning or inversion. The proposed InstructVid2Vid model modifies a pretrained image generation model, Stable Diffusion, to generate a time-dependent sequence of video frames. By harnessing the collective intelligence of disparate models, we engineer a training dataset rich in video-instruction triplets, which is a more cost-efficient alternative to collecting data in real-world scenarios. To enhance the coherence between successive frames within the generated videos, we propose the Inter-Frames Consistency Loss and incorporate it during the training process. With multimodal classifier-free guidance during the inference stage, the generated videos is able to resonate with both the input video and the accompanying instructions. Experimental results demonstrate that InstructVid2Vid is capable of generating high-quality, temporally coherent videos and performing diverse edits, including attribute editing, background changes, and style transfer. These results underscore the versatility and effectiveness of our proposed method.

The paper "InstructVid2Vid: Controllable Video Editing with Natural Language Instructions" introduces InstructVid2Vid, an end-to-end diffusion-based framework for video editing directed by natural language. This method innovatively allows for the manipulation of video content using text instructions without requiring per-example fine-tuning or inversion—processes that traditionally burden existing methods. The creators modify the Stable Diffusion model, originally designed for image generation, to accommodate video sequences, creating an approach that synthesizes a rich dataset composed of video-instruction triplets as a cost-effective alternative to real-world data collection.

Key Innovations:

1. End-to-End Framework: InstructVid2Vid provides a novel end-to-end pipeline enabling text-based video editing. Videos are edited in response to language instructions without individual video fine-tuning, offering a significant efficiency increase.
2. Dataset Synthesis: The authors develop a method to synthesize a training dataset by combining multiple foundational models such as ChatGPT for natural language processing, video caption models, and Tune-a-Video for creating temporally consistent video edits. This composite approach reduces the need for extensive real-world data collection.
3. Inter-Frames Consistency Loss: Introduced to enhance temporal coherence across video frames, this loss function focuses on minimizing disparities between consecutive frames, thus promoting smooth transitions in generated edits.
4. Inference with Multimodal Classifier-Free Guidance: The inference process exploits classifier-free guidance techniques to steer video synthesis according to both input video and textual instructions, optimizing relevance and consistency.

Experimental Results: The experimental evaluation showcases InstructVid2Vid's proficiency in performing complex video edits, such as attribute modifications, background changes, and style transfers, while maintaining high temporal consistency and video quality. The method achieves notable improvements over previous text-driven video editing models, evidenced by superior scores in NR-PSNR and FID metrics, as well as reduced frame differencing and optical flow values—highlighting enhanced frame consistency.

Contributions:

• Framework Design: InstructVid2Vid's design eliminates the typical constraints of per-example adjustments, paving the way for more generalized video editing applications.
• Efficient Data Utilization: Leveraging synthesized datasets from combined models demonstrates significant cost efficiency in training data acquisition.
• Temporal Consistency: Integration of Inter-Frames Consistency Loss effectively augments frame coherence, a notable improvement over existing methodologies.

The paper positions InstructVid2Vid as a versatile tool for video editing, adept at incorporating language instructions to generate temporally coherent and high-quality video edits without the conventional overheads associated with fine-tuning. This capability marks an advancement in video content manipulation, encouraging further exploration in language-driven video editing systems.