SceneScape – Text-driven consistent scene generation
We propose a method for text-driven perpetual view generation — synthesizing long videos of arbitrary scenes solely from an input text describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To achieve 3D consistency, i.e., generating videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene; the depth maps are used to construct a unified mesh representation of the scene, which is updated throughout the generation and is used for rendering. In contrast to previous works, which are applicable only for limited domains (e.g., landscapes), our framework generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles. Project page: this https URL
The increasing complexity and scale of machine learning (ML) has led to the need for more efficient collaboration among multiple teams. For example, when a research team invents a new architecture like “ResNet,” it is desirable for multiple engineering teams to adopt it. However, the effort required for each team to study and understand the […]
Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs) on many code related tasks, researchers have started to directly use LLMs for APR. However, prior approaches simply repeatedly sample the LLM given the same constructed input/prompt created from the original […]
Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI’s GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and […]
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