Architecture: Okinawa Beach Apartment Visualization Project

Project Overview This project involves creating architectural visualizations for a future 3-storey residential building in Okinawa, Japan. The building will feature one and two-bedroom apartments (3x per floor) with a ground-level open garage under the building. The top floor apartments will have ocean views. You will have room for creative freedom and opportunity to research on this beautiful humid subtropical island. Site Context The building will be situated on a coastal property with ocean views. Student should incorporate elements of Okinawa's tropical setting and typical building practices to create a comfortable building.  Features: Remote from big city, In sync with nature, integrated in Nakijin Village, access to local restaurants and cafes. Building Specifications 3-storey residential structure Floor 1 & 2 4x residential units per floor 2 bedrooms 1 bathrooms Living/dining area Kitchen Small balcony Approx. 64m² / 688ft² (or less) Floor 3 - same as below floors, except: 7x residential units 1x room multi-purpose, including BBQ in the balcony 1 bedroom Approx. 35m² / 380ft² (or less) Rooftop : 2/3 area solar panels 1/3 area open purpose area Can be creative or leave it empty (lower priority) Ground floor: Open parking garage Utility/storage spaces Note the blueprint can be similar to other Japanese prefectures , except Okinawa has different functional constraints as it’s very humid, warm and has frequent typhoons, so student must take it into account. Local reference development - Lions Chatan - The Lebel Naha (you may need access via VPN). Project Timeline (6 Weeks) Week 1-2: Research and concept References shared to student Student conduct briefing meeting with directional questions Concept sketching Basic site understanding Simple precedent research Week 3-4: Development Track 1: half way 3D model creation Track 2: Initial sketches or render concepts Mid-project check-in Week 5-6: Finalization Complete required deliverables Final presentation preparation

Video to Gaussian Splat Scene Converter (as docker container)

Sunny Days Technologies is seeking to develop a streamlined solution for creating game-like scenes from videos in order to enhance learner's experience. This project challenges the student to develop a comprehensive pipeline for converting single-shot video footage (drone footage, circular motion recording, linear recording, etc.) into Gaussian Splat representations. Student will research, implement, and package a complete solution as a Docker image that processes video input and generates a Gaussian Splat scene. Ideal candidates should have some notional experience with computer vision fundamentals, 3D, and be comfortable with exploring open source programs. Familiarity with Python and Docker containerization is beneficial. The core of this project involves creating an end-to-end pipeline that: Processes continuous video footage Extracts camera poses and scene geometry Optimizes a 3D Gaussian Splat representation Outputs a standardized Gaussian Splat format that can be visualized The pipeline should leverage existing open-source technologies while creating a seamless workflow. Students will need to understand and integrate various components including Structure from Motion (SfM), camera tracking, and Gaussian Splat optimization techniques. Timeline (5 weeks) Week 1: Research phase, investigating existing tools and techniques Week 2: Present working prototype Week 3: Integration, optimization, and Docker containerization Week 4 + 5: Testing, documentation, and final report preparation Relevant Open Source tools: COLMAP, gsplat, opensplat, SuperSplat, nianticlabs/spz

Interactive Mascot (character design) for Language Learning App

Our company is a startup focused on helping language learning students mastering a new language primarily via voice enabled applications. Currently we work with various OpenAI APIs and just released a language learning partner, voice based assistant you can try for yourself at  https://sunnydays.tech/#talk-to-kaki   We are looking to develop a character to take a place in being a AI-powered language partner. The idea we're exploring is a friendly female (or feminine) character to be a language partner that talks to the user. This work may involve: Research into existing language learning characters and their applications (Duolingo comes to mind) and similar app with mascots. Also explore modern AI-like amorphous characters, such as Siri / Google Gemini / OpenAI voice mode. Drafting 2 drafts of what the character may look like, to have 1 selected by the company Creating various poses that can be used by the company in the existing product For reference we have used in prototyping this character that is an invariant animation for all situations https://sunnydays.tech/animation-portrait.gif But this ended up being almost distracting with no engagement benefit. Ideally we'd have a character in a small portrait that can create connection and empathy with the user as they go through their lesson.

AI Language Learning - pronunciation training R&D

Our company is a startup focused on helping language learning students mastering a new language primarily via voice enabled applications. Currently we work with various OpenAI APIs and just released a language learning partner, voice based assistant you can try for yourself at https://sunnydays.tech/#talk-to-kaki   *Ideal candidates enjoy an open exploration challenge. Are learning or mastering 1 language in addition to their native language(s). Comfortable using Javascript|Typescript or Python.  We’re currently investigating a new feature, which you may lay down the groundwork for. The core of the feature is, given an audio file the student was just taught and are willing to repeat out loud, take a recording of their speech and compare against the original audio, while flagging any significant discrepancies where they happen. The logic may run on the FE or backend. We would like to collaborate with students to research and apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our problem. Altho to be explicit it’s not necessary to apply said techniques if a classic crafted algorithm works well. The initial language to be considered may be one of: English, Japanese, Spanish, Portuguese. At any rate consider the solution must be able to adapt into multiple mainstream languages. Input: 2 audio files of the same encoding type (something common like .mp4), a base ideal vs. user recording. Output: an array of: either points in time, or ranges, where the pronunciation didn’t seem to match input.