# Physical Simulation

Wan 2.1 AI is an open-source large-scale video generation AI model developed by Alibaba. It supports text-to-video (T2V) and image-to-video (I2V) generation, capable of transforming simple input into high-quality video content. This model is significant in the field of video generation, greatly simplifying the video creation process, lowering the creation threshold, improving creation efficiency, and providing users with a wide variety of video creation possibilities. Its main advantages include high-quality video generation effects, smooth presentation of complex actions, realistic physical simulation, and rich artistic styles. Currently, this product is fully open-source, and users can use its basic functions for free. It is highly practical for individuals and businesses that have video creation needs but lack professional skills or equipment.

Genie 2, developed by Google DeepMind, is a large-scale foundational world model capable of generating endless, operable, and playable 3D environments based on a single prompt image, designed for training and evaluating embodied agents. Genie 2 represents a significant advancement in the field of deep learning and artificial intelligence, showcasing various emergent capabilities in large-scale generative models, such as object interaction, complex character animation, and physical simulation, by simulating virtual worlds and their consequences. The research behind Genie 2 fosters new creative workflows for prototyping interactive experiences and opens up new possibilities for the development of future general AI systems and agents.

PhysGen is an innovative method for image-to-video generation that transforms a single image and input conditions (such as force and torque applied to objects in the image) into realistic, physically plausible, and temporally coherent videos. This technology achieves dynamic simulation in image space by combining model-based physical simulation with data-driven video generation processes. The main advantages of PhysGen include producing videos that are both physically and visually realistic, and offering precise control, demonstrating its superiority over existing data-driven image-to-video generation methods through quantitative comparisons and comprehensive user studies.

Kling Model is a self-developed large model with powerful video generation capabilities. It utilizes advanced technology to achieve 2-minute video generation, simulating real-world physical properties, and conceptual combination capabilities, allowing for the creation of cinematographic-quality visuals.

PhysDreamer is a method based on physics, which endows静态 3D objects with interactive dynamics by utilizing the object dynamics prior learned from video generation models. This approach allows for the simulation of realistic responses to novel interactions (such as external forces or agent operations) in the absence of real physical property data of objects. PhysDreamer promotes the development of more engaging and realistic virtual experiences through user studies to evaluate the realism of synthetic interactions.

InsActor is a role control system based on physical simulation. It can drive characters to complete various interactive tasks in complex environments through natural language instructions. The system utilizes conditional and adversarial diffusion models for multi-level planning, combining with a low-level controller to realize stable, robust control. It boasts smooth control and natural interaction, making it suitable for applications such as creative content generation, interactive entertainment, and human-computer interaction.