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We present IDC-Net (Image-Depth Consistency Network), a novel framework designed to generate RGB-D video sequences under explicit camera trajectory control. Unlike approaches that treat RGB and depth generation separately, IDC-Net jointly…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Lijuan Liu , Wenfa Li , Dongbo Zhang , Shuo Wang , Shaohui Jiao

The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to generate viewpoint-specific content from larger, immersive…

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Andrey Voynov , Amir Hertz , Moab Arar , Shlomi Fruchter , Daniel Cohen-Or

Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Zixun Fang , Kai Zhu , Zhiheng Liu , Yu Liu , Wei Zhai , Yang Cao , Zheng-Jun Zha

Recent video generative models have demonstrated impressive visual fidelity, yet they often struggle with semantic, geometric, and identity consistency. In this paper, we propose a system-level framework, termed the Divide-and-Conquer…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Haoyu Zhao , Yuang Zhang , Junqi Cheng , Jiaxi Gu , Zenghui Lu , Peng Shu , Zuxuan Wu , Yu-Gang Jiang

Spatial profiling technologies in biology, such as imaging mass cytometry (IMC) and spatial transcriptomics (ST), generate high-dimensional, multi-channel data with strong spatial alignment and complex inter-channel relationships.…

Machine Learning · Computer Science 2025-07-08 Haoran Zhang , Mingyuan Zhou , Wesley Tansey

Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometrically meaningful content. Existing approaches typically learn a mapping from camera…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Chen Hou , Christian Rupprecht

Achieving precise camera control in video generation remains challenging, as existing methods often rely on camera pose annotations that are difficult to scale to large and dynamic datasets and are frequently inconsistent with depth…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Zelin Zhao , Xinyu Gong , Bangya Liu , Ziyang Song , Jun Zhang , Suhui Wu , Yongxin Chen , Hao Zhang

State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Furthermore, without an underlying explicit geometry, these…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Dana Cohen-Bar , Ido Sobol , Raphael Bensadoun , Shelly Sheynin , Oran Gafni , Or Patashnik , Daniel Cohen-Or , Amit Zohar

Text-to-Video generation, which utilizes the provided text prompt to generate high-quality videos, has drawn increasing attention and achieved great success due to the development of diffusion models recently. Existing methods mainly rely…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Zirui Pan , Xin Wang , Yipeng Zhang , Hong Chen , Kwan Man Cheng , Yaofei Wu , Wenwu Zhu

This is a technical report on the 360-degree panoramic image generation task based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic images capture the entire $360^\circ\times 180^\circ$ field of view. So the rightmost…

Computer Vision and Pattern Recognition · Computer Science 2023-11-23 Mengyang Feng , Jinlin Liu , Miaomiao Cui , Xuansong Xie

Recent progress in video diffusion models has spurred growing interest in camera-controlled novel-view video generation for dynamic scenes, aiming to provide creators with cinematic camera control capabilities in post-production. A key…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Min-Jung Kim , Jeongho Kim , Hoiyeong Jin , Junha Hyung , Jaegul Choo

Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams…

Computer Vision and Pattern Recognition · Computer Science 2024-07-09 Teng Zhou , Yongchuan Tang

Camera control, which achieves diverse visual effects by changing camera position and pose, has attracted widespread attention. However, existing methods face challenges such as complex interaction and limited control capabilities. To…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Xiaoda Yang , Jiayang Xu , Kaixuan Luan , Xinyu Zhan , Hongshun Qiu , Shijun Shi , Hao Li , Shuai Yang , Li Zhang , Checheng Yu , Cewu Lu , Lixin Yang

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Songcen Xu , Hang Xu , Xiaodan Liang

Recently, diffusion models have demonstrated impressive capabilities in text-guided and image-conditioned image generation. However, existing diffusion models cannot simultaneously generate an image and a panoptic segmentation of objects…

Computer Vision and Pattern Recognition · Computer Science 2025-02-25 Yinghan Long , Kaushik Roy

Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed. However, existing generation models often entangle these factors, limiting independent control. In this…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Yukun Wang , Ruihuang Li , Jiale Tao , Shiyuan Yang , Liyi Chen , Zhantao Yang , Handz , Yulan Guo , Shuai Shao , Qinglin Lu

Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to…

Computer Vision and Pattern Recognition · Computer Science 2023-04-18 Mingdeng Cao , Xintao Wang , Zhongang Qi , Ying Shan , Xiaohu Qie , Yinqiang Zheng

Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Dejia Xu , Weili Nie , Chao Liu , Sifei Liu , Jan Kautz , Zhangyang Wang , Arash Vahdat

We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g., depth, edges) are insufficient: they specify layout but…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Dianbing Xi , Jiepeng Wang , Yuanzhi Liang , Xi Qiu , Jialun Liu , Hao Pan , Yuchi Huo , Rui Wang , Haibin Huang , Chi Zhang , Xuelong Li