From Video to Movie: Composite Video Editing and RHF for Quality
A new framework extending from autoregressive video generation for industry-leading video edit precision, and RHF-based generation quality.
Last updated
A new framework extending from autoregressive video generation for industry-leading video edit precision, and RHF-based generation quality.
Last updated
The Composite Video Generation (COVG) framework extends autoregressive video generation to enable precise video editing based on both text and video inputs. By encoding a reference video into a latent space and leveraging a cross-attention mechanism, COVG allows for the modification of specific features within a video while maintaining its original motion dynamics and temporal coherence. Using a combination of text embeddings and video features, the model can introduce fine-grained edits based on subtle textual cues. Empirical results demonstrate that COVG effectively adds new elements to videos, such as clouds or airplanes, while preserving the flow of the original content, outperforming traditional methods like Text-to-Video (T2V) or Image-to-Video (I2V) in terms of flexibility and control.
VideoGen-of-Thought presents a novel approach to generating long, coherent videos by dividing the process into four modules: script generation, keyframe production, shot-level video generation, and video smoothing. Inspired by the step-by-step thought process of a movie director, this framework generates videos with a clear narrative structure over extended periods. The script generation module uses large language models to create detailed textual descriptions for each shot, while the keyframe module uses diffusion models to create visually consistent frames. Shot-level video generation ensures smooth motion between frames, and the final video smoothing module handles transitions between shots. This structured approach guarantees both narrative and visual coherence, enabling the generation of high-quality, long-form videos.
The integration of rich human feedback into video post-processing provides a novel method for improving the quality of generated videos. By gathering granular, region-specific feedback on visual artifacts, motion inconsistencies, or text-video alignment issues, this approach allows for precise refinement. The feedback is used to guide a diffusion-based video refinement model, which focuses first on enhancing local features like texture and motion, and then on improving global consistency. This feedback-driven approach significantly enhances video quality, ensuring smoother transitions, better motion accuracy, and fewer visual distortions. Together, COVG, VideoGen-of-Thought, and rich feedback integration represent substantial advancements in video generation, enabling both fine-grained editing and the production of long, coherent, high-quality videos.
Building upon our autoregressive video generation framework, we aim to address the challenge of fine-grained video editing, which involves adjusting specific features of objects or modifying scenes based on subtle textual cues while preserving the original video's dynamics. Traditional methods such as Text-to-Video (T2V) and Image-to-Video (I2V) generation have demonstrated impressive results but often lack the flexibility for detailed user control. To overcome these limitations, we introduce the Composite Video Generation (COVG) approach within our autoregressive model. This framework integrates both text and video conditions into the generation process, allowing for more precise and user-controllable video editing while maintaining the consistency and temporal coherence inherent in autoregressive generation.
The COVG framework operates by encoding the reference video into a latent space, where both text and video conditions guide the generation of new video content. The core of the model consists of a Variational Autoencoder (VAE) architecture and a cross-attention mechanism that enables fine-grained control over the generated content.
Latent Representation Let the reference video be denoted as V = {v_1, v_2, ..., v_T }, where each v_i represents a frame at time i. The VAE encoder maps each frame into a latent representation z_i, forming a latent sequence Z = {z_1, z_2, ..., z_T }. The latent vectors are diffused over time using a forward diffusion process:
where \alpha_t controls the diffusion rate, and \epsilon ~ N(0, I) represents Gaussian noise. During inference, the backward denoising process generates the video from noise, conditioned on both text and video conditions.
Condition Encoding The model conditions on both text and video inputs. Text embeddings T_{text} are obtained using a pre-trained CLIP text encoder, while the video features V_{vid} are extracted using the CLIP image encoder, which operates frame-by-frame. These embeddings are processed through a query transformer with cross-attention layers, allowing the model to attend to both visual and textual inputs:
where Q, K, and V represent the query, key, and value matrices derived from both text and video embeddings. This attention mechanism enables the model to balance the contributions of both input modalities during video generation.
The overall architecture of COVG is illustrated in Figure 4, which shows the multi-stage conditioning process and the cross-attention integration between text and video inputs.
We demonstrate the effectiveness of the COVG framework by applying it to a series of video editing tasks. For each task, we provide a reference video and a textual description (delta) that describes the intended modification. The results show that our method successfully edits videos while maintaining the original motion dynamics and avoiding unnecessary artifacts.
An example case is shown in Figure 5, where the reference video depicts an erupting volcano, and the text conditions modify the video to include "black clouds" and an airplane in the sky''. The model accurately introduces these elements while preserving the natural flow of the volcanic eruption.
In this example, the model successfully adds the requested "black clouds without altering the natural flow of the volcanic eruption and accurately places an airplane in the sky''. This demonstrates the strength of our approach in handling fine-grained video edits while preserving the visual consistency of the original video.
Here, we extended our autoregressive video generation framework to support composite video editing by integrating both text and video conditions through a latent space guided by cross-attention mechanisms. Our COVG approach ensures that edits are applied in a controlled manner, preserving the original video content while introducing the requested modifications. Empirical results demonstrate that our method outperforms existing techniques, producing more accurate and semantically consistent video edits. This advancement showcases the versatility of our autoregressive model in handling complex video generation tasks, enabling fine-grained editing for practical applications.
We aim to address the issue of poor-quality video outputs from current video generation models by employing post-processing techniques guided by rich human feedback. As video generation technology advances, ensuring high visual and contextual quality becomes increasingly crucial. Despite significant progress, many generated videos still suffer from technical flaws, including low visual fidelity, lack of coherence, and inadequate content representation. This project aims to integrate human feedback into a refined post-processing pipeline to enhance the quality of these generated videos, making them more visually appealing and semantically accurate.
Current video generation models often produce unsatisfactory results due to limitations in understanding complex scenes, motion, and content requirements, as shown in Figure 6. Leveraging rich human feedback, which includes detailed assessments of visual, narrative, and contextual elements, can provide more precise guidance for refining video outputs. By incorporating human judgment, we aim to significantly improve the generated videos' overall quality, from motion to consistency and text-video alignment.
Traditional approaches [54, 40] to video evaluation rely heavily on global feedback, often reducing the complex task of video assessment to a single score for the entire clip. However, this method lacks granularity and fails to provide sufficient insights into specific problem areas within the video. To overcome this, our approach introduces two layers of feedback: the first provides localized scores, and the second identifies the exact coordinates or regions within the video where issues occur. By segmenting the video and allowing evaluators to assign scores to individual regions, we can better isolate artifacts, such as blurring, motion inconsistencies, or text-video misalignment. This region-specific feedback accelerates the identification of problematic areas, making it easier to implement targeted fixes, thereby improving the overall output.
Data collection. We start by capturing a series of videos that serve as the foundation for our refinement pipeline. Each video is processed through a specialized data pipeline that extracts key visual features. Using a video understanding model, we generate captions that describe the content of each video and then extract the first frame, which provides a representative snapshot of the video’s content. This first frame, alongside the generated caption, is fed into a closed-source model to generate a corresponding synthetic video, which acts as our input. The original, unprocessed video serves as the ground truth label for comparison. We then split the dataset into training and testing sets, ensuring that each set maintains a balance of diverse content types and complexities to thoroughly evaluate the model’s performance.
Model training. With the data prepared, we proceed with training a video refinement model using a diffusion-based framework. Our approach involves randomly masking regions of the generated videos and setting the training objective to reconstruct these masked regions without negatively affecting the surrounding unmasked areas. The diffusion framework ensures that the reconstruction process remains stable and prevents degradation in visual quality. The training process is divided into two distinct phases. In the first phase, the model focuses on reconstructing critical local features such as texture, optical flow, and motion consistency. This phase ensures that the individual segments of the video, particularly those with localized artifacts, are enhanced with greater precision. In the second phase, the refined texture and optical flow are fed into the diffusion model as conditions to guide the global video refinement process. This step ensures that the entire video output is coherent, visually consistent, and aligned with the original content while addressing feedback-driven areas that require improvement.
As our ongoing research, by integrating rich human feedback into the post-processing phase of video generation, we expect to significantly reduce visual artifacts and technical distortions in the generated videos. This approach will enhance the coherence and aesthetic appeal of the videos, leading to smoother transitions, improved motion accuracy, and more visually compelling narratives. Ultimately, this feedback-based video enhancement pipeline can be applied to a wide range of video generation models, offering scalable and adaptable improvements across various domains.
Conclusion Building upon autoregressive modeling techniques, we introduced several advancements to improve video generation and editing. First, the Composite Video Generation (COVG) framework allows for precise, fine-grained video editing by integrating both text and video inputs, enabling users to modify specific elements of a video while preserving its original dynamics and temporal consistency. Next, we proposed the VideoGen-of-Thought framework, which generates long-form, coherent videos by breaking the process into manageable stages, including script generation, keyframe production, and shot-level video creation. By leveraging large language models and advanced video generation methods, this approach produces high-quality, narrative-driven videos with consistent transitions and detailed visuals across frames. Lastly, we introduced a feedback-driven refinement process that incorporates rich human feedback to enhance video quality, addressing issues such as visual artifacts and motion inconsistencies. These innovations together ensure greater control, improved coherence, and higher overall video quality, pushing the boundaries of both video generation and editing.
