663 件 ・ 関連度順
概要と使いどころ
Use when planning an AAMAS project timeline from venue fit through abstract, full-paper or extended-abstract upload, supplementary material, the double-blind rebuttal, decision, IFAAMAS camera-ready, registration, and presentation, with track-specific deadlines and backward-planning offsets for a game-theory-plus-experiments paper.
日本語の概要は準備中です。原文の説明を表示しています。
Use when planning an AAAI project timeline from topic selection through abstract, OpenReview submission, two-phase review, rebuttal, decision, camera-ready, registration, presentation, and public artifact release.
日本語の概要は準備中です。原文の説明を表示しています。
Render a 'hand-swipe flavor listicle' video from a config — the brand's REAL product cutouts each float on a flat flavor-matched color field under a persistent title and slide RIGHT-TO-LEFT one to the next like a phone-feed scroll, while a green-screen hand clip (back of hand toward camera, index finger up) is trimmed to its clean finger-up window, looped once per flavor, keyed with a green-dominance alpha key, scaled down and anchored at the bottom, with an optional end card and an optional instrumental music bed — deterministic PIL plus FFmpeg assembly, FREE (the hand clip comes from create-video-fal and the bed from create-music-elevenlabs), so labels stay pixel-crisp. Use for the hand-swipe-flavor-listicle format.
日本語の概要は準備中です。原文の説明を表示しています。
Generate an AI creator talking to camera, saying an approved script, as one continuous track — H3 Max reference-to-video through the GooseWorks fal-proxy (bills the Ads agent). Plans takes on line boundaries under H3's 15s cap, dry-runs a cost estimate, generates the first take alone so its voice can be locked and passed to every later take, joins takes with measured 0.10s dissolves, and moves each line's timing onto the words actually spoken. Use for any format with a generated creator speaking a script (split-screen, screen inserts, talking-head ads).
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with camera, photos, audio, haptics, ShazamKit, on-device music analysis, face grouping and video highlights, screen recording, casting to non-AirPlay routes, SharePlay playback, the user's Apple Music library, lock-screen metadata, or CarPlay app design, templates, and navigation. Covers AVCaptureSession, AVFoundation, Core Haptics, MediaPlayer, MusicKit, NowPlaying, ScreenCaptureKit, DockKit.
日本語の概要は準備中です。原文の説明を表示しています。
Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.
日本語の概要は準備中です。原文の説明を表示しています。
Maintain and extend existing SceneKit 3D scenes and visualizations. Use when working with SCNView, SCNScene, SCNNode scene graphs, SceneKit geometry/materials/lights/cameras, SCNAction animation, SCNPhysicsBody physics, SCNParticleSystem effects, .scn/.dae/.abc SceneKit assets, shader modifiers, or SwiftUI SceneView. SceneKit is soft-deprecated and in maintenance mode; route new apps, significant updates, USD/USDZ pipelines, and migration planning toward RealityKit.
日本語の概要は準備中です。原文の説明を表示しています。
Build iOS augmented reality and 3D experiences with RealityKit and ARKit. Use when adding RealityView content, loading entities or USDZ models, anchoring objects to planes or world positions, distinguishing entity hit tests from ARKit real-world raycasts, handling AR camera availability, world tracking, scene updates, or RealityKit entity gestures and interactions.
日本語の概要は準備中です。原文の説明を表示しています。
Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and CoreMLRequest/VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
日本語の概要は準備中です。原文の説明を表示しています。
Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).
日本語の概要は準備中です。原文の説明を表示しています。
Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes. Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage. NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).
日本語の概要は準備中です。原文の説明を表示しています。
Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive skill for Three.js 3D web development. Use this skill when building interactive 3D scenes, WebGL/WebGPU applications, product configurators, 3D visualizations, or immersive web experiences. Triggers on tasks involving Three.js, 3D rendering, scenes, cameras, meshes, materials, lights, animations, textures, or WebGL/WebGPU rendering.
日本語の概要は準備中です。原文の説明を表示しています。
Use when animating characters or scenes in Godot 4.7: AnimationPlayer clips and RESET, AnimationTree state machines and blend spaces built in code, Mixamo or glTF import, loop settings, BoneMap retargeting and shared animation libraries, root motion, foot sliding, foot IK with TwoBoneIK3D or custom SkeletonModifier3D, tweens, SpringArm3D cameras, cutscenes; or when a character T-poses, slides, floats, freezes mid-blend or a transition never fires.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building 3D levels and worlds in Godot 4.7: third-person or FPS controller, player stuck on stairs, spring arm camera clipping, blockout with CSG, GridMap and MeshLibrary, terrain (heightmap, HeightMapShape3D, Terrain3D regions, textures, painting), scattering trees with MultiMesh, streaming a big world, procedural meshes with SurfaceTool, Jolt physics settings, importing GLB or glTF from Blender or AI generators (Scenario, Tripo, Meshy, Hitem3D), import scripts, name suffixes, model facing, collision generation, LODs.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building a 2D game in Godot 4.7: make a platformer or top-down controller that feels right (coyote time, jump buffer, jump height), TileMapLayer and TileSet in code, terrains and autotiles that pick wrong tiles, a dual grid, a camera that jitters or stutters, pixel art that looks blurry or uneven, stretch and integer scaling, 2D lights and shadows, y-sort order wrong, parallax gaps, or importing AI-generated sprites and tilesets from Scenario.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an Unreal Engine 5.8 project misses its frame rate or stutters: "optimize to 60 fps", "we run at 38 fps", CPU or GPU bound, game or render thread too long, hitches, traversal or shader stutter (PSO), GC spikes, camera-cut spikes, Lumen, Nanite, VSM, ray tracing, foliage, crowds or Niagara too expensive, async compute, stat unit, ProfileGPU, Unreal Insights, TraceQuery, snapshothitches, scalability and device profiles, dynamic resolution and TSR, streaming budgets, or proving a fix with before and after captures.
日本語の概要は準備中です。原文の説明を表示しています。
Use when producing a video ad with Scenario from a product shot or brand assets: a mobile 9:16 or landscape 16:9 commercial, a TikTok, Reels, Shorts, YouTube, or CTV spot, or campaign creative needing a creative brief, storyboard, cinematic camera work, brand-faithful product footage, music and voiceover, captions, ad variants, budget planning, or platform delivery specs. Keywords: video ad, commercial, product video, advert, hook, CTA, end card, campaign, ad creative.
日本語の概要は準備中です。原文の説明を表示しています。
Use when generating or editing images with Grok Imagine models on Scenario via MCP: text-to-image, instruction editing with up to three reference images, posters, packaging, ads, or infographics with exact in-image text, photoreal shots from camera and lighting cues, extended aspect ratios up to 20:9 and 9:20, quality and resolution tiers, or batches of variations. Keywords: Grok Imagine Image 2.0, Image Quality, xAI, Aurora, Grok, txt2img, img2img, typography, 2K.
日本語の概要は準備中です。原文の説明を表示しています。
Use when generating or editing video with MiniMax Hailuo models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference images, videos, or audio, native stereo audio, bracketed camera commands, extending a clip, inserting a new scene into one, or recasting the people in it from photos. Keywords: MiniMax, Hailuo 3.0, H3, H3 Max, Turbo, Extend Video, Insert Video, Recast, Hailuo 2.3, T2V, I2V, V2V, 2K.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a single image of a character, creature, prop, vehicle, or building must be seen from new camera angles through the Scenario MCP server: turnarounds, a 360 orbit, views from behind, above, below, or straight down, consistent multi-angle renders on the same grounded background or on transparency, or novel views that go through a 3D intermediary instead of guessing.
日本語の概要は準備中です。原文の説明を表示しています。
Use when retexturing an existing 3D asset with PATINA PBR materials through the Scenario MCP server: a GLB or Blender scene (an image-to-3D result with flat colors, a prop, a diorama) whose surfaces need basecolor, normal, roughness, metalness, and height maps per material family with geometry untouched, or a matched before-and-after comparison film rendered in Blender with one camera path, lighting, and cut. Keywords: PATINA, PBR retexture, material families, Blender EEVEE, comparison video.
日本語の概要は準備中です。原文の説明を表示しています。
Use when generating or editing video with Runway models on Scenario via MCP: text-to-video or image-to-video from a first frame with Gen4.5 (cinematic motion, sequenced actions, camera choreography), or video-to-video editing of real footage with Aleph 2 (remove objects, swap products, restyle or relight shots, keyframe-pinned reference images, motion and audio preserved). Keywords: Runway ML, Gen4.5, Gen-4.5, Aleph, T2V, I2V, V2V, first frame.
日本語の概要は準備中です。原文の説明を表示しています。