World models that support controllable
and editable spatiotemporal environments are valuable
for robotics, enabling scalable training data, repro ducible evaluation, and flexible task design. While
recent text-to-video models generate realistic dynam ics, they are constrained to 2D views and offer limited
interaction. We introduce MorphoSim, a language guided framework that generates 4D scenes with
multi-view consistency and object-level controls. From
natural language instructions, MorphoSim produces
dynamic environments where objects can be directed,
recolored, or removed, and scenes can be observed
from arbitrary viewpoints. The framework integrates
trajectory-guided generation with feature field dis tillation, allowing edits to be applied interactively
without full re-generation. Experiments show that Mor phoSim maintains high scene fidelity while enabling
controllability and editability. The code is available
at https://github.com/eric-ai-lab/Morph4D.
Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work, we propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capab…
Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes. Besides, they only achieve controllable transfer within predefined fixed regions. This paper emphasizes the transfer of makeup details and steps towards more flexible controls. To this end, we propose Exquisite and locally editable GAN for makeup transfer (EleGANt). It encodes facial attributes into pyramidal feature maps to preserves high-frequency information. It uses attention to extract makeup features from the reference and adapt th…
PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Beihang University（北京航空航天大学）；The Hong Kong University of Science and Technology（香港科技大学）；Independent Researcher（独立研究者）；Shanghai Jiao Tong University（上海交通大学）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent（腾讯）
Text-driven person image generation is an emerging and challenging task in cross-modality image generation. Controllable person image generation promotes a wide range of applications such as digital human interaction and virtual try-on. However, previous methods mostly employ single-modality information as the prior condition (e.g. pose-guided person image generation), or utilize the preset words for text-driven human synthesis. Introducing a sentence composed of free words with an editable semantic pose map to describe person appearance is a more user-friendly way. In this paper, we propose H…
Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories
University of Tübingen（图宾根大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute（ELLIS研究所）；Tübingen AI Center（图宾根人工智能中心）
Recent methods (e.g. MaterialGAN) have used unconditional GANs to generate per-pixel material maps, or as a prior to reconstruct materials from input photographs. These models can generate varied random material appearance, but do not have any mechanism to constrain the generated material to a specific category or to control the coarse structure of the generated material, such as the exact brick layout on a brick wall. Furthermore, materials reconstructed from a single input photo commonly have artifacts and are generally not tileable, which limits their use in practical content creation pipel…
Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints
Hong Kong University of Science and Technology（香港理工大学）；LightIllusion, China（中国LightIllusion公司）；Alibaba Group（阿里巴巴集团）；Simon Fraser University, Canada（加拿大Simon Fraser大学）
Follow-Your-MultiPose: Tuning-Free Multi-Character Text-to-Video Generation via Pose Guidance
School of Big Data and Software Engineering, Chongqing University（重庆大学大数据与软件学院）；The Hong Kong University of Science and Technology（香港科技大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shanghai Jiao Tong University（上海交通大学）
Lighting-grounded Video Generation with Renderer-based Agent Reasoning
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（北京大学计算机学院国家视觉技术工程研究中心）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；OpenBayes Information Technology Co., Ltd.（北京开贝信息技术有限公司）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
University of Tübingen（图宾根大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Snap Inc.（Snap公司）
We present a novel method for performing flexible, 3D-aware image content manipulation while enabling high-quality novel view synthesis. While NeRF-based approaches are effective for novel view synthesis, such models memorize the radiance for every point in a scene within a neural network. Since these models are scene-specific and lack a 3D scene representation, classical editing such as shape manipulation, or combining scenes is not possible. Hence, editing and combining NeRF-based scenes has not been demonstrated. With the aim of obtaining interpretable and controllable scene representations…
OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai AI Lab（上海人工智能实验室）；Beihang University（北京航空航天大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong（香港中文大学）
Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images
The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology(Guangzhou)（香港科技大学（广州））
