SRU-NET: SOBEL RESIDUAL U-NET FOR IMAGE MANIPULATION DETECTION

Recently, most successful image manipulation detection methods have been based on convolutional neural networks (CNNs). Nevertheless, Existing CNN methods have limited abilities. CNN-based detection networks tend to extract signal features strongly related to content. However, image manipulation detection tends to extract weak signal features that are weakly related to content. To address this issue, We propose a novel Sobel residual neural network with adaptive central difference convolution, an extension of the classical U-Net architecture, for image manipulation detection. Adaptive central differential convolution can capture the essential attributes of an image by gathering intensity and gradient information. Sobel residual gradient block can capture forgery edge discriminative details. Extensive experimental results show that our method can significantly improve the accuracy of localising the forged region compared with the state-of-the-art methods

Controllable Motion Diffusion Model

Generating realistic and controllable motions for virtual characters is a challenging task in computer animation, and its implications extend to games, simulations, and virtual reality. Recent studies have drawn inspiration from the success of diffusion models in image generation, demonstrating the potential for addressing this task. However, the majority of these studies have been limited to offline applications that target at sequence-level generation that generates all steps simultaneously. To enable real-time motion synthesis with diffusion models in response to time-varying control signals, we propose the framework of the Controllable Motion Diffusion Model (COMODO). Our framework begins with an auto-regressive motion diffusion model (A-MDM), which generates motion sequences step by step. In this way, simply using the standard DDPM algorithm without any additional complexity, our framework is able to generate high-fidelity motion sequences over extended periods with different types of control signals. Then, we propose our reinforcement learning-based controller and controlling strategies on top of the A-MDM model, so that our framework can steer the motion synthesis process across multiple tasks, including target reaching, joystick-based control, goal-oriented control, and trajectory following. The proposed framework enables the real-time generation of diverse motions that react adaptively to user commands on-the-fly, thereby enhancing the overall user experience. Besides, it is compatible with the inpainting-based editing methods and can predict much more diverse motions without additional fine-tuning of the basic motion generation models. We conduct comprehensive experiments to evaluate the effectiveness of our framework in performing various tasks and compare its performance against state-of-the-art methods

Cinematic Behavior Transfer via NeRF-based Differentiable Filming

In the evolving landscape of digital media and video production, the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections, neglecting 3D statuses. To address these issues, we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities, which also achieves a higher rating in the user study.Comment: Project Page: https://virtualfilmstudio.github.io/projects/cinetransfe