Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual relationships exclusively into the reverse process, often disregarding their relevance in the forward process. This inconsistency between forward and reverse processes may limit the precise conveyance of textual semantics in visual synthesis results. To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes. We propagate this context to all timesteps in the two processes to adapt their trajectories, thereby facilitating cross-modal conditional modeling. We generalize our contextualized diffusion to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing. In each task, our ContextDiff achieves new state-of-the-art performance, significantly enhancing the semantic alignment between text condition and generated samples, as evidenced by quantitative and qualitative evaluations. Our code is available at https://github.com/YangLing0818/ContextDiff
Korea University（高丽大学）；NVIDIA Corporation（英伟达公司）；KAIST（韩国科学技术院）
The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually not semantically meaningful due to the difficulty of determining the direction and magnitude in the StyleGAN latent space. In this paper, we propose a framework to generate realistic videos by leveraging multimodal (sound-image-text) embedding space. As sound provides the temporal contexts of the scene, our framework learns to generate a video that is semantically consistent with sound. First, our sound inversion modu…
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Trento（特伦托大学）
Generation of realistic high-resolution videos of human subjects is a challenging and important task in computer vision. In this paper, we focus on human motion transfer - generation of a video depicting a particular subject, observed in a single image, performing a series of motions exemplified by an auxiliary (driving) video. Our GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose. Temporal consistency is maintained by further …
Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation
Monash University（墨尔本大学）；Harbin Institute of Technology（哈尔滨工业大学）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗quentcourt研究所）；Rajiv Gandhi University（拉朱·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）
Beuth University of Applied Sciences Berlin（柏林博伊特应用科学大学）
We propose a novel approach for the synthesis of sign language videos using GANs. We extend the previous work of Stoll et al. by using the human semantic parser of the Soft-Gated Warping-GAN from to produce photorealistic videos guided by region-level spatial layouts. Synthesizing target poses improves performance on independent and contrasting signers. Therefore, we have evaluated our system with the highly heterogeneous MS-ASL dataset with over 200 signers resulting in a SSIM of 0.893. Furthermore, we introduce a periodic weighting approach to the generator that reactivates the training and …
Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Auckland（奥克兰大学）；National University of Singapore（新加坡国立大学）
UC Santa Barbara（加州大学圣巴巴拉分校）；Meta；NEC Laboratories America（NEC美国实验室）
Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which re…
Text-guided generative diffusion models unlock powerful image creation and editing tools. While these have been extended to video generation, current approaches that edit the content of existing footage while retaining structure require expensive re-training for every input or rely on error-prone propagation of image edits across frames. In this work, we present a structure and content-guided video diffusion model that edits videos based on visual or textual descriptions of the desired output. Conflicts between user-provided content edits and structure representations occur due to insufficient…
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
The Chinese University of Hong Kong（香港中文大学）；International Digital Economy Academy (IDEA)（国际数字经济学院）；The University of Hong Kong（香港大学）；Tsinghua University（清华大学）
Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；School of Computer Science and Engineering, NTU（南洋理工大学计算机科学与工程学院）
University of California, Berkeley（加州大学伯克利分校）；National University of Singapore（新加坡国立大学）；Meta；Tencent Holdings Ltd and Tsinghua University（腾讯控股有限公司和清华大学）
Hong Kong University of Science and Technology（香港科技大学）；Microsoft Research Asia（微软亚洲研究院）；City University of Hong Kong（香港城市大学）
We propose a novel framework to produce cartoon videos by fetching the color information from two input keyframes while following the animated motion guided by a user sketch. The key idea of the proposed approach is to estimate the dense cross-domain correspondence between the sketch and cartoon video frames, and employ a blending module with occlusion estimation to synthesize the middle frame guided by the sketch. After that, the input frames and the synthetic frame equipped with established correspondence are fed into an arbitrary-time frame interpolation pipeline to generate and refine addi…
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
RGFVR: Reference-Guided Face Video Restoration with Flow Matching
Chair of Media Technology, Munich Institute of Robotics and Machine Intelligence, School of Computation, Information, and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院慕尼黑机器人与机器智能研究所媒体技术教席）
Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network …
We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as …
University of Chinese Academy of Sciences（中国科学院大学）；The Institute of Microelectronics, Tsinghua University（清华大学微电子研究所）；Chinese Academy of Sciences Artificial Intelligence Research（中国科学院人工智能研究院）
This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing realistic videos conditioned on given texts. Existing video generation methods cannot be easily adapted to handle this task well, due to the frame discontinuity issue and their text-free generation schemes. To address these problems, we propose a recurrent deconvolutional generative adversarial network (RD-GAN), which includes a recurrent deconvolutional network (RDN) as the generator and a 3D convolutional neural networ…
We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another which generates low-level maps via a pipeline of down-sampling, a newly devised soft edge detector, and a novel lossless compression scheme. For decoding, we use a standard video decoder as well as a neural network based one, which is trained using a conditional GAN. Recent "deep" approaches to video compression require multiple videos to pre-train generative networks to conduct interpolation. In contrast to this prior w…
Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval
Johns Hopkins Applied Physics Laboratory（约翰霍普金斯应用物理实验室）；Johns Hopkins University（约翰霍普金斯大学）；Human Language Technology Center of Excellence（人类语言技术卓越中心）；DEVCOM Army Research Laboratory（陆军研究实验室）
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes sub-optimal for learning spatio-temporally fine-grained features in videos, where scenes and instances evolve through space and time. In this paper, we present Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively learn spatio-temporally fine-grained video representations via self-supervision. We first design a region-based pretext task which requires …
While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we t…
