VideoNeuMat: Neural Material Extraction from Generative Video Models

Creating photorealistic materials for 3D rendering requires exceptional artistic skill. Generative models for materials could help, but are currently limited by the lack of high-quality training data. While recent video generative models effortlessly produce realistic material appearances, this knowledge remains entangled with geometry and lighting. We present VideoNeuMat, a two-stage pipeline that extracts reusable neural material assets from video diffusion models. First, we finetune a large video model (Wan 2.1 14B) to generate material sample videos under controlled camera and lighting trajectories, effectively creating a "virtual gonioreflectometer" that preserves the model's material realism while learning a structured measurement pattern. Second, we reconstruct compact neural materials from these videos through a Large Reconstruction Model (LRM) finetuned from a smaller Wan 1.3B video backbone. From 17 generated video frames, our LRM performs single-pass inference to predict neural material parameters that generalize to novel viewing and lighting conditions. The resulting materials exhibit realism and diversity far exceeding the limited synthetic training data, demonstrating that material knowledge can be successfully transferred from internet-scale video models into standalone, reusable neural 3D assets.

Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation

Generating high-fidelity human videos that match user-specified identities is important yet challenging in the field of generative AI. Existing methods often rely on an excessive number of training parameters and lack compatibility with other AIGC tools. In this paper, we propose Stand-In, a lightweight and plug-and-play framework for identity preservation in video generation. Specifically, we introduce a conditional image branch into the pre-trained video generation model. Identity control is achieved through restricted self-attentions with conditional position mapping, and can be learned quickly with only 2000 pairs. Despite incorporating and training just $\sim$1% additional parameters, our framework achieves excellent results in video quality and identity preservation, outperforming other full-parameter training methods. Moreover, our framework can be seamlessly integrated for other tasks, such as subject-driven video generation, pose-referenced video generation, stylization, and face swapping.

An Improved NeuMIP with Better Accuracy

Neural reflectance models are capable of accurately reproducing the spatially-varying appearance of many real-world materials at different scales. However, existing methods have difficulties handling highly glossy materials. To address this problem, we introduce a new neural reflectance model which, compared with existing methods, better preserves not only specular highlights but also fine-grained details. To this end, we enhance the neural network performance by encoding input data to frequency space, inspired by NeRF, to better preserve the details. Furthermore, we introduce a gradient-based loss and employ it in multiple stages, adaptive to the progress of the learning phase. Lastly, we utilize an optional extension to the decoder network using the Inception module for more accurate yet costly performance. We demonstrate the effectiveness of our method using a variety of synthetic and real examples.