一个可以在犀牛3D建模软件中直接调用的模型生成插件，允许用户通过文字描述生成3D模型，并提供友好的用户界面和多种调整参数的功能。
dreamhopper的特点:
- 1. 直接集成于犀牛3D软件中
- 2. 基于文字生成3D模型
- 3. 支持raymarching技术
- 4. 用户友好的界面
dreamhopper的功能:
- 1. 在犀牛3D中安装并启用dreamhopper插件
- 2. 输入文字描述以生成对应的3D模型
- 3. 调整生成模型的参数以满足设计需求
相关导航
Nname: “Scribble-Supervised LiDAR Semantic Segmentation” description: “A method for semantic segmentation using scribble annotations on LiDAR data.” url: “https://github.com/ouenal/scribblekitti” features: – “Utilizes scribble annotations for training.” – “Designed for LiDAR data processing.” – “Improves segmentation accuracy through supervised learning.” usage: – “Apply scribble annotations to LiDAR point clouds.” – “Train models to enhance semantic segmentation performance.” name: “Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model” description: “Amodal segmentation model that generalizes across tasks and distributions.” url: “https://github.com/YihongSun/Bayesian-Amodal” features: – “Incorporates Bayesian approaches for uncertainty estimation.” – “Handles out-of-task and out-of-distribution challenges.” – “Focuses on amodal segmentation tasks.” usage: – “Train on diverse datasets to improve generalization.” – “Utilize the model for segmentation in various applications.” name: “Intrinsic Neural Fields: Learning Functions on Manifolds” description: “A framework for learning intrinsic representations of data on manifolds.” url: “https://github.com/tum-vision/intrinsic-neural-fields” features: – “Learns functions directly on manifold structures.” – “Enables better representation of complex data.” – “Supports various neural network architectures.” usage: – “Model complex geometric shapes in 3D.” – “Apply to tasks requiring manifold learning.” name: “BigColor: Colorization using a Generative Color Prior for Natural Images” description: “A generative model for colorizing grayscale images based on learned priors.” url: “https://github.com/KIMGEONUNG/BigColor” features: – “Generative approach for image colorization.” – “Utilizes learned color priors from natural images.” – “Produces high-quality colorized outputs.” usage: – “Convert black-and-white images to color.” – “Enhance historical photos with colorization.” name: “Implicit Neural Representations for Variable Length Human Motion Generation” description: “Generates human motions using implicit neural representations.” url: “https://github.com/PACerv/ImplicitMotion” features: – “Supports variable length motion generation.” – “Uses implicit representations for modeling dynamics.” – “Facilitates realistic human motion synthesis.” usage: – “Create animations for characters in games.” – “Simulate human movements for virtual environments.” name: “BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation” description: “A strategy for domain adaptation focusing on class balance.” url: “https://github.com/ispc-lab/BMD” features: – “Class-balanced approach for improved adaptation.” – “Multicentric dynamic prototypes for flexibility.” – “Does not require source data for adaptation.” usage: – “Apply in scenarios with domain shifts.” – “Enhance model performance across different datasets.” name: “Improving Choral Music Separation through Expressive Synthesized Data from Sampled Instruments” description: “A method for separating choral music using synthesized data.” url: “https://github.com/RetroCirce/Choral_Music_Separation” features: – “Utilizes synthesized data for training.” – “Improves separation quality in choral music.” – “Focuses on expressive music elements.” usage: – “Separate vocal tracks in choral recordings.” – “Enhance music production workflows.” name: “M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection” description: “A transformer-based model for detecting deepfakes across multiple modalities.” url: “https://github.com/wangjk666/PyDeepFakeDet” features: – “Multi-modal capabilities for deepfake detection.” – “Utilizes multi-scale transformers for robustness.” – “Addresses various deepfake generation techniques.” usage: – “Detect deepfake videos in real-time.” – “Analyze content across different media types.” name: “Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance” description: “A GAN-based model for generating 3D face representations.” url: “https://github.com/aashishrai3799/3DFaceCAM” features: – “Disentangles shape and appearance for control.” – “Generates realistic 3D face models.” – “Allows for customization of facial features.” usage: – “Create 3D avatars for virtual reality.” – “Generate diverse face representations for games.” name: “A Simple Baseline for BEV Perception Without LiDAR” description: “A baseline method for BEV perception using camera data.” url: “https://github.com/aharley/bev_baseline” features: – “Uses camera data for BEV perception.” – “No reliance on LiDAR technology.” – “Simple yet effective baseline model.” usage: – “Perform perception tasks in autonomous driving.” – “Integrate with existing camera systems for analysis.” name: “A Comprehensive Assessment of Dialog Evaluation Metrics” description: “A study providing a thorough evaluation of dialog metrics.” url: “https://github.com/exe1023/DialEvalMetrics” features: – “Comprehensive analysis of dialog evaluation metrics.” – “Framework for assessing dialog systems.” – “Provides benchmarks for future research.” usage: – “Evaluate performance of conversational agents.” – “Inform development of dialog systems.”-对对话评估指标的全面评估
一项提供对对话指标全面评估的研究，为未来研究提供基准。
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