27 accepted papers
Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transition capabilities remain rudimentary and unstable, largely confining generated videos to single-shot sequences. In this w…
Shot transitions play a pivotal role in multi-shot video generation, as they determine the overall narrative expression and the directorial design of visual storytelling.However, recent progress has primarily focused on low-level visual consistency across shots, neglecting how transitions are design
Image protection against unauthorized diffusion-based editing has achieved encouraging progress. However, existing methods face two critical limitations: (1) They only disturb the denoising direction at local step, resulting in generated images still retaining original or edited semantics. (2) Their
The rapid development of generative models has significantly advanced image and video applications. Among these, video creation, aimed at generating videos under various conditions, has gained substantial attention. However, existing video creation models either focus solely on a few specific condit
Pose-agnostic anomaly detection (PAD) achieves strong performance in localizing anomalies from arbitrary viewpoints when trained on densely sampled normal data. However, under sparse-view conditions, existing methods face two key challenges: (1) sparse observations lead to overfitting and geometric
Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hindered by the limited availability of sensor data and resource constraints of edge systems. While transferring pre-trained models to different sensing ap…
Diffusion models have achieved significant progress in the task of image animation due to their powerful generative capabilities. However, preserving appearance consistency to the static input image, and avoiding abrupt motion change in the generated animation, remains challenging. In this paper, we…
Existing image-to-3D creation methods typically split the task into two individual stage: multi-view image generation and 3D reconstruction, leading to two main limitations: (1) In multi-view generation stage, the multi-view generated images present a challenge to preserving 3D consistency;; (2) In…
The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models to input prompts highlights the critical role of prompt design in influencing generative outcomes. Prior research has pr…
Diffusion models have driven the advancement of vision generation over the past years. However, it is often difficult to apply these large models in downstream tasks, due to massive fine-tuning cost. Recently, Low-Rank Adaptation (LoRA) has been applied for efficient tuning of diffusion models. Unfo…
The Mamba model excels in anomaly detection through efficient long-range dependency modeling and linear complexity. However, Mamba-based anomaly detectors still face two critical challenges: (1) insufficient modeling of diverse local features leading to inaccurate detection of subtle anomalies; (2)…
Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior knowledge from multiple diffusion models, resulting in inconsis…
Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generat…
Recent works have successfully extended large-scale text-to-image models to the video domain, producing promising results but at a high computational cost and requiring a large amount of video data. In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based…
Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate consistent multiviews but they have reduced generation speed a…
This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. InternVid contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accomp…
Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips ("shot-level'') depicting a single scene. To deliver a coherent long video ("story-level''), it is desirable to have creative transition and pre…
While super-resolution (SR) methods based on diffusion models exhibit promising results their practical application is hindered by the substantial number of required inference steps. Recent methods utilize the degraded images in the initial state thereby shortening the Markov chain. Nevertheless the…
Video generation has witnessed significant advancements yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should pro…
In this work we present Vlogger a generic AI system for generating a minute-level video blog (i.e. vlog) of user descriptions. Different from short videos with a few seconds vlog often contains a complex storyline with diversified scenes which is challenging for most existing video generation approa…
We consider the problem of generating musical soundtracks in sync with rhythmic visual cues. Most existing works rely on pre-defined music representations, leading to the incompetence of generative flexibility and complexity. Other methods directly generating video-conditioned waveforms suffer from…
"Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The m…
Knowledge models endeavor to improve representation and feature extraction capabilities while keeping low computational cost. Firstly, existing embedding models in hypercomplex spaces of non-Abelian group are optimized. Then a method for fast quaternion multiplication is proposed with proof, with wh…
Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. However, existing methods for font generation are often in supervised learning. They require a large number of paired data,…
This paper targets at a task of composed query image retrieval. Given a composed query consists of a reference image and modification text, the task aims to retrieve images which are generally similar to the reference image but differ according to the given modification text. The task is challenging…
Watermark is one of the most fundamental approaches for avoiding potential copyright infringement activities. However, whether its introduction would effect the understanding of deep learning models remains unstudied. In this work, we propose a visible adversarial attack method that transforms and p…
This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to detect the objects of interests and to convert the object from source domain to another domain. In contrast, we decompo…