Writings, Papers and Blogs on Text Models

Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References ABSTRACT Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper

Table of Links Abstract and 1. Introduction 2. Related Work 2.1. NeRF Editing and 2.2. Inpainting Techniques 2.3. Text-Guided Visual Content Generation 3. Method 3.1. Training View Pre-processing 3.2. Progressive Training 3.3. 4D Extension 4. Experiments and 4.1. Experimental Setups 4.2. Ablation and comparison 5. Conclusion and 6. References 3.2. Progressive Training Warmup Training. Our training image pre-processing stage provides a good initialization for rough convergence. Before fine-tuning on these images to get fine convergence,

Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: [email protected]); (2) Hassan Mansoor, Google Research (e-mail: [email protected]); (3) Victor Carbune, Google Research (e-mail: [email protected]); (4) Peter Chen, Google Research and Equal leadership contribution ([email protected]); (5) Tony Mak, Google Research and Equal leadership contribution (e-mail: [email protected]). Table of Links Abstract and Introduction BIG-Bench Mistake Benchmark results Backtracking Related Works Conclusion,

Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: [email protected]); (2) Hassan Mansoor, Google Research (e-mail: [email protected]); (3) Victor Carbune, Google Research (e-mail: [email protected]); (4) Peter Chen, Google Research and Equal leadership contribution ([email protected]); (5) Tony Mak, Google Research and Equal leadership contribution (e-mail: [email protected]). Table of Links Abstract and Introduction BIG-Bench Mistake Benchmark results Backtracking Related Works Conclusion,

Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: [email protected]); (2) Hassan Mansoor, Google Research (e-mail: [email protected]); (3) Victor Carbune, Google Research (e-mail: [email protected]); (4) Peter Chen, Google Research and Equal leadership contribution ([email protected]); (5) Tony Mak, Google Research and Equal leadership contribution (e-mail: [email protected]). Table of Links Abstract and Introduction BIG-Bench Mistake Benchmark results Backtracking Related Works Conclusion,

Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: [email protected]); (2) Hassan Mansoor, Google Research (e-mail: [email protected]); (3) Victor Carbune, Google Research (e-mail: [email protected]); (4) Peter Chen, Google Research and Equal leadership contribution ([email protected]); (5) Tony Mak, Google Research and Equal leadership contribution (e-mail: [email protected]). Table of Links Abstract and Introduction BIG-Bench Mistake Benchmark results Backtracking Related Works Conclusion,

Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: [email protected]); (2) Hassan Mansoor, Google Research (e-mail: [email protected]); (3) Victor Carbune, Google Research (e-mail: [email protected]); (4) Peter Chen, Google Research and Equal leadership contribution ([email protected]); (5) Tony Mak, Google Research and Equal leadership contribution (e-mail: [email protected]). Table of Links Abstract and Introduction BIG-Bench Mistake Benchmark results Backtracking Related Works Conclusion,