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bw_0075 · natural_landscape

模型实际输入详情 · 两个模型的 inputs 文件逐字节一致,已核验。

原始题目全文

以下逐字展示 query.txt,保留原文语言;这是任务要求,并非生成结果或评分标准。

我在做一套房产户型的营销效果图,客厅主墙想挂一幅这种蓝调油画风的山景装饰画,客户下周要过方案,得靠它把空间衬得通透、显大。麻烦照着这张参考图还原一个层峦叠嶂、空气透视很强的自然山地。主体是由近及远大约五到六层连绵起伏的丘陵山脊:前景走深靛蓝、暗蓝绿,往中景过渡到蓝绿,最远近地平线处是浅青蓝,越远越淡、越亮、对比越弱,做出那种雾蒙蒙的纵深。山形要平缓起伏、别有尖锐岩石,保留柔和的笔触质感。天空占上半幅,从上到下的蓝色渐变,点几团蓬松、偏冷白的积云。重点就是这层层推远的空气透视和整体明亮通透的冷调子——挂上去要能让人觉得开阔。材质统一走哑光漫反射,配点体积雾拉开远近,一盏偏高的柔和主光加天空环境光,色温偏冷。相机近似平视、略带广角,比例按原图 3:2,下山上天。渲染用 Cycles,开降噪,出图别低于 2400×1600(3:2)。整体氛围你把握,大众看着舒服、有点高级感就行。最后给我一张成品渲染图,.blend 工程也留着,方便后面微调。
下载 query.txt

原始参考素材

natural_landscape原始参考图片
references/image_01.jpg · 点击查看原图

任务附件

本题没有额外附件。

本次运行附加的执行指令

这是自建 harness 传给两模型的共同执行指令,不属于原始题目。CPU 设置与个别原题 GPU 要求之间的差异,详见主页说明。

Complete the Blender creation task in /workspace/inputs/query.txt using all declared references and attachments in /workspace/inputs/input.json. Read the reference images with Read; for video, extract and inspect frames with ffmpeg and Read. Blender 4.2.10 is available as `blender` or /runtime/blender/blender. Python with Pillow/NumPy is available as `python`. You can write and run bpy scripts with background Blender. This machine uses CPU Cycles; use 8 threads, and inspect your renders before finishing. You have an 8-hour time budget. Fulfil the original requested deliverables, including animations and resolutions where specified. Save the editable primary project to /workspace/exports/final.blend, with requested images/videos and all texture dependencies inside /workspace/exports using relative paths or packed assets. Put temporary scripts and previews under /workspace/work. Do not finish while required rendering jobs are still running. Use only the provided task inputs, your tools, and your own creation work; evaluation is separate.

完整输入文件

下载本题完整输入 ZIP

文件(点击下载)大小
input.json164 B
instruction.html845,279 B
query.txt1,211 B
reference_images.base64.json841,705 B
references/image_01.jpg630,962 B
查看 input.json 文件清单
{
  "task_id": "bw_0075",
  "query_file": "query.txt",
  "files": [
    "references/image_01.jpg",
    "instruction.html",
    "reference_images.base64.json"
  ]
}

instruction.html 和 reference_images.base64.json 为输入目录内的原始文件,已保留供下载。本页不包含评测 rubric 或模型产物。