Tuesday, December 10, 2024

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How to use Sora,...

MIT Technology Review’s How To series helps you get things done.  Today, OpenAI released its...

The US Department of...

The US Department of Defense has invested $2.4 million over two years...

OpenAI’s new defense contract...

At the start of 2024, OpenAI’s rules for how armed forces might...

Google DeepMind’s new AI...

Google DeepMind has unveiled an AI model that’s better at predicting the...
HomeMachine LearningRoomDreamer: Text-Driven 3D...

RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture



The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose “RoomDreamer”, which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our work addresses the challenge of synthesizing both geometry and texture aligned to the input scene structure and prompt simultaneously. The key insight is that a scene should be treated as a whole, taking into account both scene texture and geometry. The proposed framework consists of two significant…



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Memory-Retaining Finetuning via Distillation

This paper was accepted at the Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML) Workshop at NeurIPS 2024. Large language models (LLMs) pretrained on large corpora of internet text possess much of the world's knowledge. Following pretraining, one...

Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling

Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To...

Towards Time-Series Reasoning with LLMs

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance...