10 citations · 23 across the 15 of their papers we have counts for
15 papers
Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes
Minh-Quan Le, Armand Comas, Alexandros Lattas +7
Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws , each modality reshapes the other. In this paper, we…
SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers
Zhengcong Fei, Hao Jiang, Di Qiu +8
The generation and editing of audio-conditioned talking portraits guided by multimodal inputs, including text, images, and videos, remains under explored. In this paper, we present…
SkyReels-V2: Infinite-length Film Generative Model
Guibin Chen, Dixuan Lin, Jiangping Yang +22
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual qual…
SkyReels-A2: Compose Anything in Video Diffusion Transformers
Zhengcong Fei, Debang Li, Di Qiu +8
This paper presents SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized…
SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers
Di Qiu, Zhengcong Fei, Rui Wang +5
We present SkyReels-A1, a simple yet effective framework built upon video diffusion Transformer to facilitate portrait image animation. Existing methodologies still encounter issue…
Ingredients: Blending Custom Photos with Video Diffusion Transformers
Zhengcong Fei, Debang Li, Di Qiu +2
This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as Ing…
Video Diffusion Transformers are In-Context Learners
Zhengcong Fei, Di Qiu, Debang Li +2
This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. Specifically, we propose a sim…
MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis
Di Qiu, Zheng Chen, Rui Wang +4
Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time appli…
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning
Di Qiu, Xinyang Lin, Kaiye Wang +2
With the growing attention on data privacy and communication security in face recognition applications, federated learning has been introduced to learn a face recognition model wit…
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
Kun Wu, Ning Liu, Zhen Zhao +5
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collect…
LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation
Yuxiang Bao, Di Qiu, Guoliang Kang +4
Leveraging the generative ability of image diffusion models offers great potential for zero-shot video-to-video translation. The key lies in how to maintain temporal consistency ac…
Learn to Cluster Faces via Pairwise Classification
Junfu Liu, Di Qiu, Pengfei Yan +1
Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performanc…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Shixiang Tang, Peng Su, Dapeng Chen +1
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algori…
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
Yuanhan Zhang, Zhenfei Yin, Jing Shao +22
As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, fa…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Peng Su, Shixiang Tang, Peng Gao +3
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, the poor ability of adapting to dynamic environments remains a major challenge f…