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NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion



Novel view synthesis from a single image requires inferring occluded regions of objects and scenes while simultaneously maintaining semantic and physical consistency with the input. Existing approaches condition neural radiance fields (NeRF) on local image features, projecting points to the input image plane, and aggregating 2D features to perform volume rendering. However, under severe occlusion, this projection fails to resolve uncertainty, resulting in blurry renderings that lack details. In this work, we propose NerfDiff, which addresses this issue by distilling the knowledge of a 3D-aware…



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Computational Bottlenecks of Training Small-Scale Large Language Models

This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) workshop at NeurIPS Workshop 2024. While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and...

On Device Llama 3.1 with Core ML

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user's device for cost-effective inference,...

Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In...