Source-linked AI summary
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, Jingren Zhou
TL;DR
Video synthesis still struggles with coherent semantics, detail continuity, and aligned guidance. I2VGen-XL addresses these challenges with a cascaded image-to-video approach, producing high-quality videos with more reasonable and significant motions across diverse data.
Problem
Video synthesis remains challenged by coherent spatial and motion semantics, detail continuity, and insufficiently aligned video-text pairs.
Method
I2VGen-XL uses a static image-guided cascaded pipeline with a semantic-preserving base stage and a detail-refining 1280×720 stage.
Results
I2VGen-XL produces more reasonable and significant motions while preserving image content, spatial detail, temporal coherence, and meaningful motions across diverse categories.
Takeaways & Limitations
The approach demonstrates high-quality single-image-to-video synthesis across a broad range of category data.
Takeaways & Limitations
The model’s ability to comprehend user inputs such as captions or images remains limited, making interaction more difficult.
Abstract
from arXiv · showhide
Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280$\times$720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at \url{https://i2vgen-xl.github.io}.
1. Introduction
I2VGen-XL is introduced as a cascaded image-to-video method that uses static images to reduce dependence on aligned text-video data while separating semantic generation from video refinement. Its two-stage design combines hierarchical image encoders, high-resolution refinement, and large-scale image/video data to improve video quality and diversity.
- Motivation: Existing cascaded approaches can learn similar distributions at each stage when they optimize the same objective with identical inputs, introducing non-negligible noise into generated videos.The introduction identifies this lack of explicit task decoupling as a limitation of one existing approach category.
- Method overview: I2VGen-XL uses a single static image as the primary condition to reduce reliance on well-aligned text-video pairs.The method is designed to generate high-definition videos with coherent spatial and motion dynamics and continuous details.
- Base stage: The base stage targets semantic coherence at low resolution while preserving the content and identity information of input images.It uses a fixed CLIP encoder and a learnable content encoder to extract high-level semantics and low-level details for a video diffusion model.
- Refinement stage: The refinement stage raises video resolution to 1280×720 and refines details and artifacts using a distinct video diffusion model.This stage is intended to improve resolution and visual detail after base-stage generation.
- Data and evaluation: 35 million high-quality single-shot videos and 6 billion images are collected to enhance I2VGen-XL’s diversity and robustness across common daily-life categories.The paper also evaluates the method across diverse data and benchmarks it against current top-ranked methods.
2. Related Works
Related work spans diffusion-based image and video synthesis, including language-conditioned generation, editing, and controllable generation. I2VGen-XL is positioned as an alternative focused on improving image-to-video quality for video content creation.
- Diffusion probabilistic models: Diffusion probabilistic models learn target probability distributions and have demonstrated greater diversity and realism than GANs and VAEs.
- Image synthesis via diffusion models: Mainstream diffusion-based image generation uses language-model features with cross-attention to condition generated content.Stable Diffusion and DALL-E 2 use CLIP text encoders, while Imagen uses T5.
- Image synthesis via diffusion models: Related image-generation branches include text-based editing and controllable generation with additional conditions.Examples include Imagic and Sine for editing, and ControlNet and Composer for controllable generation.
- Video synthesis via diffusion models: Video-generation research has progressed from GAN-related methods to diffusion models, while spatio-temporal coherence and realism remain significant challenges.
- Video synthesis via diffusion models: I2VGen-XL focuses on enhancing image-to-video quality, particularly for video content creation, as an alternative approach.
3. I2VGen-XL
I2VGen-XL is a cascaded video-generation framework that separates semantic consistency from visual refinement. Its base stage preserves input-image content at low resolution, while its refinement stage restores details and produces 1280 × 720 videos using brief text conditioning.
- Base stage: The base stage targets semantic consistency and content preservation by learning high-level semantics and low-level image details at 448×256 resolution.It uses multi-level feature extraction to comprehend image intent while preserving content effectively.
- Base stage: CLIP visual features capture high-level semantics but can poorly preserve the input image’s content and structure because their alignment objective overlooks fine details.This motivates supplementing semantic features with local image features.
- Base stage: VQGAN encoder features are added directly to the first-frame input noise, improving preservation of image details and content over a more complex semantic encoder.The encoder is used because it can fully reconstruct the original image.
- Refinement stage: The refinement stage applies a separate high-resolution VLDM with SDEdit-style noising and denoising to videos generated by the base stage.It uses user-provided brief text rather than the original image as conditioning, because changing the condition improves restorative capability.
4. Experiments
Experiments train I2VGen-XL on large, quality-sorted video-text and image-text datasets, then evaluate its motion, identity preservation, qualitative generalization, and human-body robustness against leading methods. Results show realistic, diverse motions and accurate content preservation, while revealing an identity-preservation trade-off and limited video-text data as a broader challenge.
- Datasets: 35 million videos and 6 billion images train I2VGen-XL across 360p–2k resolutions, sorted by aesthetic score, motion intensity, and main-subject proportion.The data combine public and private video-text and image-text datasets.
- Comparison with top methods: I2VGen-XL produces richer, more realistic motions than Gen-2 and Pika, although those baselines preserve input-object identity more successfully.The comparison uses pseudo-factual, real, and abstract paintings generated through the methods’ web interfaces.
- Refinement analysis: The refinement process reduces noise in local details, and frequency analysis examines its spatial and temporal changes in generated videos.The analysis reports that low-quality video has a noise-like high-frequency distribution, whereas high-quality video has a closer resemblance to the described reference distribution.
- Qualitative analysis: Across human faces, 3D cartoons, anime, Chinese paintings, and animals, generated videos preserve image content and aesthetics while exhibiting meaningful, accurate motions.Examples include accurate mouth movements and a woodblock bird rotating its head while retaining the original style.
- Human-body robustness: Human-body experiments yield reasonably realistic predictions and motions while preserving most characteristic body features.The experiments specifically assess robustness on human-body images, described as a major challenge in video synthesis.
5. Conclusion
The report introduces I2VGen-XL, a cascaded model that generates high-quality videos from a single static image by separately addressing semantic consistency and spatio-temporal continuity. Its effectiveness is validated on substantial category data, while the report also explores a new video-synthesis paradigm.
- 5. Conclusion: I2VGen-XL generates high-quality videos from a single static image.The model is presented as a cascaded video synthesis approach.
- 5. Conclusion: The model separately addresses semantic consistency and spatio-temporal continuity through two stages.The two stages are designed to serve these two purposes separately.
- 5. Conclusion: I2VGen-XL’s effectiveness is validated using a substantial amount of category data.
- 5. Conclusion: The report explores a new paradigm of video synthesis.